Effects of physiotherapy and home-based training in parkinsonian syndromes: protocol for a randomised controlled trial (MobilityAPP)

INTRODUCTION

Gait and mobility impairment are pivotal signs of parkinsonism, and they are particularly severe in atypical parkinsonian disorders including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). A pilot study demonstrated a significant improvement of gait in patients with MSA of parkinsonian type (MSA-P) after physiotherapy and matching home-based exercise, as reflected by sensor-based gait parameters. In this study, we aim to investigate whether a gait-focused physiotherapy (GPT) and matching home-based exercise lead to a greater improvement of gait performance compared with a standard physiotherapy/home-based exercise programme (standard physiotherapy, SPT).

METHODS AND ANALYSIS

This protocol was deployed to evaluate the effects of a GPT versus an active control undergoing SPT and matching home-based exercise with regard to laboratory gait parameters, physical activity measures and clinical scales in patients with Parkinson's disease (PD), MSA-P and PSP. The primary outcomes of the trial are sensor-based laboratory gait parameters, while the secondary outcome measures comprise real-world derived parameters, clinical rating scales and patient questionnaires. We aim to enrol 48 patients per disease group into this double-blind, randomised-controlled trial. The study starts with a 1 week wearable sensor-based monitoring of physical activity. After randomisation, patients undergo a 2 week daily inpatient physiotherapy, followed by 5 week matching unsupervised home-based training. A 1 week physical activity monitoring is repeated during the last week of intervention.

ETHICS AND DISSEMINATION

This study, registered as 'Mobility in Atypical Parkinsonism: a Trial of Physiotherapy (Mobility_APP)' at clinicaltrials.gov (NCT04608604), received ethics approval by local committees of the involved centres. The patient's recruitment takes place at the Movement Disorders Units of Innsbruck (Austria), Erlangen (Germany), Lausanne (Switzerland), Luxembourg (Luxembourg) and Bolzano (Italy). The data resulting from this project will be submitted to peer-reviewed journals, presented at international congresses and made publicly available at the end of the trial.

TRIAL REGISTRATION NUMBER

NCT04608604.

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study

BACKGROUND

Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies.

OBJECTIVE

The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors.

METHODS

Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors.

RESULTS

The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids.

CONCLUSIONS

Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health.

TRIAL REGISTRATION

ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2021-050785.

Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet

Progressive gait impairment is common in aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1,000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised gait detection method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults.

It needs more than a myocardial infarction to start exercising: the CoLaus|PsyCoLaus prospective study

BACKGROUND

Increased physical activity (PA) is recommended after an acute coronary event to prevent recurrences. Whether patients with acute coronary event actually increase their PA has not been assessed using objective methods such as accelerometer. We aimed to assess the subjectively and objectively measured physical activity (PA) levels of patients before and after an acute coronary event.

METHODS

Data from the three follow-up surveys of a prospective study conducted in Lausanne, Switzerland. Self-reported PA was assessed by questionnaire in the first (2009-2012) and second (2014-2017) follow-ups. Objective PA was assessed by a wrist-worn accelerometer in the second and third (2018-2021) follow-ups. Participants who developed an acute coronary event between each survey period were considered as eligible. PA levels were compared before and after the event, and changes in PA levels were also compared between participants who developed an acute event with three gender and age-matched healthy controls.

RESULTS

For self-reported PA, data from 43 patients (12 women, 64 ± 9 years) were used. No differences were found for all PA levels expressed in minutes/day before and after the event: moderate PA, median and [interquartile range] 167 [104-250] vs. 153 [109-240]; light PA: 151 [77-259] vs. 166 [126-222], and sedentary behaviour: 513 [450-635] vs. 535 [465-642] minutes/day. Comparison with gender- and age-matched healthy controls showed no differences regarding trends in reported PA. For accelerometer-assessed PA, data from 32 patients (16 women, 66 ± 9 years) were used. No differences were found for all PA levels expressed in minutes/day before and after the event: moderate PA: 159 [113-189] vs. 141 [111-189]; light PA: 95.8 [79-113] vs. 95.9 [79-117], and sedentary behaviour: 610 [545-659] vs. 602 [540-624]. Regarding the comparison with gender- and age-matched healthy controls, controls had an increase in accelerometer-assessed sedentary behaviour as % of day: multivariable adjusted average standard error 2.7 ± 0.6, while no increase was found for cases: 0.1 ± 1.1; no differences were found for the other PA levels.

CONCLUSION

Patients do not seem to change their PA levels after a first coronary event. Our results should be confirmed in larger samples.

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases

Introduction

The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings.

Methods

Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data.

Results and discussion

The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings

Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

BACKGROUND

Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.

METHODS

Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated.

RESULTS

We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms.

CONCLUSIONS

Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation

BACKGROUND

Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety.

METHODS

The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks.

RESULTS

The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life.

CONCLUSIONS

The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy.

TRIAL REGISTRATION

ISRCTN-12246987.

Impact of adherence to a lifestyle-integrated programme on physical function and behavioural complexity in young older adults at risk of functional decline: a multicentre RCT secondary analysis

CONTEXT

Long-term adherence to physical activity (PA) interventions is challenging. The Lifestyle-integrated Functional Exercise programmes were adapted Lifestyle-integrated Functional Exercise (aLiFE) to include more challenging activities and a behavioural change framework, and then enhanced Lifestyle-integrated Functional Exercise (eLiFE) to be delivered using smartphones and smartwatches.

OBJECTIVES

To (1) compare adherence measures, (2) identify determinants of adherence and (3) assess the impact on outcome measures of a lifestyle-integrated programme.

DESIGN, SETTING AND PARTICIPANTS

A multicentre, feasibility randomised controlled trial including participants aged 61-70 years conducted in three European cities.

INTERVENTIONS

Six-month trainer-supported aLiFE or eLiFE compared with a control group, which received written PA advice.

OUTCOME MEASURES

Self-reporting adherence per month using a single question and after 6-month intervention using the Exercise Adherence Rating Scale (EARS, score range 6-24). Treatment outcomes included function and disability scores (measured using the Late-Life Function and Disability Index) and sensor-derived physical behaviour complexity measure. Determinants of adherence (EARS score) were identified using linear multivariate analysis. Linear regression estimated the association of adherence on treatment outcome.

RESULTS

We included 120 participants randomised to the intervention groups (aLiFE/eLiFE) (66.3±2.3 years, 53% women). The 106 participants reassessed after 6 months had a mean EARS score of 16.0±5.1. Better adherence was associated with lower number of medications taken, lower depression and lower risk of functional decline. We estimated adherence to significantly increase basic lower extremity function by 1.3 points (p<0.0001), advanced lower extremity function by 1.0 point (p<0.0001) and behavioural complexity by 0.008 per 1.0 point higher EARS score (F(3,91)=3.55, p=0.017) regardless of group allocation.

CONCLUSION

PA adherence was associated with better lower extremity function and physical behavioural complexity. Barriers to adherence should be addressed preintervention to enhance intervention efficacy. Further research is needed to unravel the impact of behaviour change techniques embedded into technology-delivered activity interventions on adherence.

TRIAL REGISTRATION NUMBER

NCT03065088.

Walking and running cadence estimation using a single trunk-fixed accelerometer for daily physical activities assessment

Accurate assessment of the type, duration, and intensity of physical activity (PA) in daily life is considered very important because of the close relationship between PA level, health, and well-being. Therefore, the assessment of PA using lightweight wearable sensors has gained interest in recent years. In particular, the use of activity monitors could help to measure the health-related effects of specific PA interventions. Our study, named as Run4Vit, focuses on evaluating the acute and longterm effects of an eight-week running intervention on PA behaviour and vitality. To achieve this goal, we developed an algorithm to detect running and estimate instantaneous cadence using a single trunk-fixed accelerometer. Cadence was computed using time and frequency domain approaches. Validation was performed over a wide range of locomotion speeds using an open-source gait database. Across all subjects, the cadence estimation algorithms achieved a mean bias and precision of - 0.01 ± 0.69 steps/min for the temporal method and 0.02 ± 1.33 steps/min for the frequency method. The running detection algorithm demonstrated very good performance, with an accuracy of 98% and a precision superior to 99%. These algorithms could be used to extract metrics related to the multiple dimensions of PA, and provide reliable outcome measures for the Run4Vit longitudinal running intervention program. Clinical Relevance- This work aims at validating a multi-dimensional physical activity (PA) classification algorithm for assessing the acute and long-term effects of eight weeks running intervention on PA behaviours and vitality.

Dual-Task Treadmill Training for the Prevention of Falls in Parkinson's Disease: Rationale and Study Design

Various factors, such as fear of falling, postural instability, and altered executive function, contribute to the high risk of falling in Parkinson's disease (PD). Dual-task training is an established method to reduce this risk. Motor-perceptual task combinations typically require a patient to walk while simultaneously engaging in a perceptual task. Motor-executive dual-tasking (DT) combines locomotion with executive function tasks. One augmented reality treadmill training (AR-TT) study revealed promising results of a perceptual dual-task training with a markedly reduced frequency of falls especially in patients with PD. We here propose to compare the effects of two types of concurrent tasks, perceptual and executive, on high-intensity TT). Patients will be trained with TT alone, in combination with an augmented reality perceptual DT (AR-TT) or with an executive DT (Random Number Generation; RNG-TT). The results are expected to inform research on therapeutic strategies for the training of balance in PD.

Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue

Understanding the influence of running-induced acute fatigue on the homeostasis of the body is essential to mitigate the adverse effects and optimize positive adaptations to training. Fatigue is a multifactorial phenomenon, which influences biomechanical, physiological, and psychological facets. This work aimed to assess the evolution of these three facets with acute fatigue during a half-marathon. 13 recreational runners were equipped with one inertial measurement unit (IMU) on each foot, one combined global navigation satellite system-IMU-electrocardiogram sensor on the chest, and an Android smartphone equipped with an audio recording application. Spatio-temporal parameters for the running gait, along with the heart rate, its variability and complexity were computed using validated algorithms. Perceived fatigability was assessed using the rating-of-fatigue (ROF) scale at every 10 min of the race. The data was split into eight equal segments, corresponding to at least one ROF value per segment, and only level running parts were retained for analysis. During the race, contact time, duty factor, and trunk anteroposterior acceleration increased, and the foot strike angle and vertical stiffness decreased significantly. Heart rate showed a progressive increase, while the metrics for heart rate variability and complexity decreased during the race. The biomechanical parameters showed a significant alteration even with a small change in perceived fatigue, whereas the heart rate dynamics altered at higher changes. When divided into two groups, the slower runners presented a higher change in heart rate dynamics throughout the race than the faster runners; they both showed similar trends for the gait parameters. When tested for linear and non-linear correlations, heart rate had the highest association with biomechanical parameters, while the trunk anteroposterior acceleration had the lowest association with heart rate dynamics. These results indicate the ability of faster runners to better judge their physiological limits and hint toward a higher sensitivity of perceived fatigue to neuromuscular changes in the running gait. This study highlights measurable influences of acute fatigue, which can be studied only through concurrent measurement of biomechanical, physiological, and psychological facets of running in real-world conditions.

Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors

Long-term monitoring of real-life physical activity (PA) using wearable devices is increasingly used in clinical and epidemiological studies. The quality of the recorded data is an important issue, as unreliable data may negatively affect the outcome measures. A potential source of bias in PA assessment is the non-wearing of a device during the expected monitoring period. Identification of non-wear time is usually performed as a pre-processing step using data recorded by the accelerometer, which is the most common sensor used for PA analysis algorithms. The main issue is the correct differentiation between non-wear time, sleep time, and sedentary wake time, especially in frail older adults or patient groups. Based on the current state of the art, the objectives of this study were to (1) develop robust non-wearing detection algorithms based on data recorded with a wearable device that integrates acceleration and temperature sensors; (2) validate the algorithms using real-world data recorded according to an appropriate measurement protocol. A comparative evaluation of the implemented algorithms indicated better performances (99%, 97%, 99%, and 98% for sensitivity, specificity, accuracy, and negative predictive value, respectively) for an event-based detection algorithm, where the temperature sensor signal was appropriately processed to identify the timing of device removal/non-wear.

Technical validation of real-world monitoring of gait: a multicentric observational study

INTRODUCTION

Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device.

METHODS AND ANALYSIS

This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment.

ETHICS AND DISSEMINATION

The study has been granted ethics approval by the centre's committees (London-Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available.

TRIAL REGISTRATION NUMBER

ISRCTN (12246987).

Real-world gait speed estimation, frailty and handgrip strength: a cohort-based study

Gait speed is a reliable outcome measure across multiple diagnoses, recognized as the 6th vital sign. The focus of the present study was on assessment of gait speed in long-term real-life settings with the aim to: (1) demonstrate feasibility in large cohort studies, using data recorded with a wrist-worn accelerometer device; (2) investigate whether the walking speed assessed in the real-world is consistent with expected trends, and associated with clinical scores such as frailty/handgrip strength. This cross-sectional study included n = 2809 participants (1508 women, 1301 men, [45-75] years old), monitored with a wrist-worn device for 13 consecutive days. Validated algorithms were used to detect the gait bouts and estimate speed. A set of metrics were derived from the statistical distribution of speed of gait bouts categorized by duration (short, medium, long). The estimated usual gait speed (1-1.6 m/s) appears consistent with normative values and expected trends with age, gender, BMI and physical activity levels. Speed metrics significantly improved detection of frailty: AUC increase from 0.763 (no speed metrics) to 0.798, 0.800 and 0.793 for the 95th percentile of individual's gait speed for bout durations < 30, 30-120 and > 120 s, respectively (all p < 0.001). Similarly, speed metrics also improved the prediction of handgrip strength: AUC increase from 0.669 (no speed metrics) to 0.696, 0.696 and 0.691 for the 95th percentile of individual's gait speed for bout durations < 30, 30-120 and > 120 s, respectively (all p < 0.001). Forward stepwise regression showed that the 95th percentile speed of gait bouts with medium duration (30-120 s) to be the best predictor for both conditions. The study provides evidence that real-world gait speed can be estimated using a wrist-worn wearable system, and can be used as reliable indicator of age-related functional decline.

Instrumented 5-Time Sit-To-Stand Test: Parameters Predicting Serious Falls beyond the Duration of the Test

BACKGROUND

Falls are a major cause of injuries in older adults. To evaluate the risk of falls in older adults, clinical assessments such as the 5-time sit-to-stand (5xSTS) test can be performed. The development of inertial measurement units (IMUs) has provided the possibility of a more in-depth analysis of the movements' biomechanical characteristics during this test. The goal of the present study was to investigate whether an instrumented 5xSTS test provides additional information to predict multiple or serious falls compared to the conventional stopwatch-based method.

METHODS

Data from 458 community-dwelling older adults were analyzed. The participants were equipped with an IMU on the trunk to extract temporal, kinematic, kinetic, and smoothness movement parameters in addition to the total duration of the test by the stopwatch.

RESULTS

The total duration of the test obtained by the IMU and the stopwatch was in excellent agreement (Pearson's correlation coefficient: 0.99), while the total duration obtained by the IMU was systematically 0.52 s longer than the stopwatch. In multivariable analyses that adjusted for potential confounders, fallers had slower vertical velocity, reduced vertical acceleration, lower vertical power, and lower vertical jerk than nonfallers. In contrast, the total duration of the test measured by either the IMU or the stopwatch did not differ between the 2 groups.

CONCLUSIONS

An instrumented 5xSTS test provides additional information that better discriminates among older adults those at risk of multiple or serious falls than the conventional stopwatch-based assessment.

Algorithms for Walking Speed Estimation Using a Lower-Back-Worn Inertial Sensor: A Cross-Validation on Speed Ranges

Walking/gait speed is a key measure for daily mobility characterization. To date, various studies have attempted to design algorithms to estimate walking speed using an inertial sensor worn on the lower back, which is considered as a proper location for activity monitoring in daily life. However, these algorithms were rarely compared and validated on the same datasets, including people with different preferred walking speed. This study implemented several original, improved, and new algorithms for estimating cadence, step length and eventually speed. We designed comprehensive cross-validation to compare the algorithms for walking slow, normal, fast, and using walking aids. We used two datasets, including reference data for algorithm validation from an instrumented mat (40 subjects) and shanks-worn inertial sensors (88 subjects), with normal and impaired walking patterns. The results showed up to 50% performance improvements. Training of algorithms on data from people with different preferred speeds led to better performance. For the slow walkers, an average RMSE of 2.5 steps/min, 0.04 m, and 0.10 m/s were respectively achieved for cadence, step length, and speed estimation. For normal walkers, the errors were 3.5 steps/min, 0.08 m, and 0.12 m/s. An average RMSE of 1.3 steps/min, 0.05 m, and 0.10 m/s were also observed on fast walkers. For people using walking aids, the error significantly increased up to an RMSE of 14 steps/min, 0.18 m, and 0.27 m/s. The results demonstrated the robustness of the proposed combined speed estimation approach for different speed ranges. It achieved an RMSE of 0.10, 0.18, 0.15, and 0.32 m/s for slow, normal, fast, and using walking aids, respectively.

Indirect Estimation of Breathing Rate from Heart Rate Monitoring System during Running

Recent advances in wearable technologies integrating multi-modal sensors have enabled the in-field monitoring of several physiological metrics. In sport applications, wearable devices have been widely used to improve performance while minimizing the risk of injuries and illness. The objective of this project is to estimate breathing rate (BR) from respiratory sinus arrhythmia (RSA) using heart rate (HR) recorded with a chest belt during physical activities, yielding additional physiological insight without the need of an additional sensor. Thirty-one healthy adults performed a run at increasing speed until exhaustion on an instrumented treadmill. RR intervals were measured using the Polar H10 HR monitoring system attached to a chest belt. A metabolic measurement system was used as a reference to evaluate the accuracy of the BR estimation. The evaluation of the algorithms consisted of exploring two pre-processing methods (band-pass filters and relative RR intervals transformation) with different instantaneous frequency tracking algorithms (short-term Fourier transform, single frequency tracking, harmonic frequency tracking and peak detection). The two most accurate BR estimations were achieved by combining band-pass filters with short-term Fourier transform, and relative RR intervals transformation with harmonic frequency tracking, showing 5.5% and 7.6% errors, respectively. These two methods were found to provide reasonably accurate BR estimation over a wide range of breathing frequency. Future challenges consist in applying/validating our approaches during in-field endurance running in the context of fatigue assessment.

Clinical value of assessing motor performance in postacute stroke patients

Background

Rehabilitative treatment plans after stroke are based on clinical examinations of functional capacity and patient-reported outcomes. Objective information about daily life performance is usually not available, but it may improve therapy personalization.

Objective

To show that sensor-derived information about daily life performance is clinically valuable for counseling and the planning of rehabilitation programs for individual stroke patients who live at home. Performance information is clinically valuable if it can be used as a decision aid for the therapeutic management or counseling of individual patients.

Methods

This was an observational, cross-sectional case series including 15 ambulatory stroke patients. Motor performance in daily life was assessed with body-worn inertial sensors attached to the wrists, shanks and trunk that estimated basic physical activity and various measures of walking and arm activity in daily life. Stroke severity, motor function and activity, and degree of independence were quantified clinically by standard assessments and patient-reported outcomes. Motor performance was recorded for an average of 5.03 ± 1.1 h on the same day as the clinical assessment. The clinical value of performance information is explored in a narrative style by considering individual patient performance and capacity information.

Results

The patients were aged 59.9 ± 9.8 years (mean ± SD), were 6.5 ± 7.2 years post stroke, and had a National Institutes of Health Stroke Score of 4.0 ± 2.6. Capacity and performance measures showed high variability. There were substantial discrepancies between performance and capacity measures in some patients.

Conclusions

This case series shows that information about motor performance in daily life can be valuable for tailoring rehabilitative therapy plans and counseling according to the needs of individual stroke patients. Although the short recording time (average of 5.03 h) limited the scope of the conclusions, this study highlights the usefulness of objective measures of daily life performance for the planning of rehabilitative therapies. Further research is required to investigate whether information about performance in daily life leads to improved rehabilitative therapy results.

Real-world speed estimation using single trunk IMU: methodological challenges for impaired gait patterns

Walking speed (WS) is recognized as an important dimension of functional health and a candidate endpoint for clinical trials. To be adopted as a powerful outcome measure in clinical assessment, WS should be estimated pervasively and accurately in the real-life context. Although current state of the art points to possible solutions, e.g., by using pairing of wearable sensors with dedicated algorithms, the accuracy and robustness of existing algorithms in challenging situations should be carefully considered. This study highlights the main methodological issues for WS estimation using single inertial sensor fixed on trunk (chest/low back) and data recorded in a sample of stroke patients with impaired mobility.

Walking Speed of Children and Adolescents With Cerebral Palsy: Laboratory Versus Daily Life

The purpose of this pilot study was to compare walking speed, an important component of gait, in the laboratory and daily life, in young individuals with cerebral palsy (CP) and with typical development (TD), and to quantify to what extent gait observed in clinical settings compares to gait in real life. Fifteen children, adolescents and young adults with CP (6 GMFCS I, 2 GMFCS II, and 7 GMFCS III) and 14 with TD were included. They wore 4 synchronized inertial sensors on their shanks and thighs while walking at their spontaneous self-selected speed in the laboratory, and then during 2 week-days and 1 weekend day in their daily environment. Walking speed was computed from shank angular velocity signals using a validated algorithm. The median of the speed distributions in the laboratory and daily life were compared at the group and individual levels using Wilcoxon tests and Spearman's correlation coefficients. The corresponding percentile of daily life speed equivalent to the speed in the laboratory was computed and observed at the group level. Daily-life walking speed was significantly lower compared to the laboratory for the CP group (0.91 [0.58-1.23] m/s vs 1.07 [0.73-1.28] m/s, p = 0.015), but not for TD (1.29 [1.24-1.40] m/s vs 1.29 [1.20-1.40] m/s, p = 0.715). Median speeds correlated highly in CP (p < 0.001, rho = 0.89), but not in TD. In children with CP, 60% of the daily life walking activity was at a slower speed than in-laboratory (corresponding percentile = 60). On the contrary, almost 60% of the daily life activity of TD was at a faster speed than in-laboratory (corresponding percentile = 42.5). Nevertheless, highly heterogeneous behaviors were observed within both populations and within subgroups of GMFCS level. At the group level, children with CP tend to under-perform during natural walking as compared to walking in a clinical environment. The heterogeneous behaviors at the individual level indicate that real-life gait performance cannot be directly inferred from in-laboratory capacity. This emphasizes the importance of completing clinical gait analysis with data from daily life, to better understand the overall function of children with CP.

The association of basic and challenging motor capacity with mobility performance and falls in young seniors

BACKGROUND

Understanding the association between motor capacity (MC) (what people can do in a standardized environment), mobility performance (MP) (what people actually do in real-life) and falls is important for early detection of and counteracting on functional decline, particularly in the rapidly growing population of young seniors. Therefore, this study aims to 1) explore the association between MC and MP, and between MC and falls, and 2) investigate whether challenging MC measures are better associated with MP and falls than basic MC measures.

METHODS

Basic (habitual gait speed, Timed Up-and-Go) and challenging (fast gait speed, Community Balance & Mobility Scale) MC measures were performed in 169 young seniors (61-70 years). MP was assessed using one-week sensor-monitoring including time being sedentary, light active, and at least moderately active. Falls in the previous six months were reported. Associations and discriminative ability were calculated using correlation, regression and receiver operating curve analysis.

RESULTS

Mean age was 66.4 (SD 2.4) years (50.6 % women). Small to moderate associations (r = 0.06-0.31; p < .001-.461) were found between MC, MP and falls. Challenging MC measures showed closer associations with MP and falls (r = 0.10-0.31; p < .001-.461) compared to basic (r = 0.06-0.22; p = .012-.181), remained significant in three out of four regression models explaining 2.5-8.6 % of the variance, and showed highest discriminative ability (area under the curve = 0.59-0.70) in all analyses.

CONCLUSIONS

Challenging MC measures are closer associated with mobility performance and falls as compared to basic MC measures in young seniors. This indicates the importance of applying challenging motor capacity assessments in young seniors. On the same note, small to moderate associations imply a need for an assessment of both MC and MP in order to capture the best possible MC and the actual daily-life MP in young seniors.

Comparison of gait characteristics between clinical and daily life settings in children with cerebral palsy

Gait assessments in standardized settings, as part of the clinical follow-up of children with cerebral palsy (CP), may not represent gait in daily life. This study aimed at comparing gait characteristics in laboratory and real life settings on the basis of multiple parameters in children with CP and with typical development (TD). Fifteen children with CP and 14 with TD wore 5 inertial sensors (chest, thighs and shanks) during in-laboratory gait assessments and during 3 days of daily life. Sixteen parameters belonging to 8 distinct domains were computed from the angular velocities and/or accelerations. Each parameter measured in the laboratory was compared to the same parameter measured in daily life for walking bouts defined by a travelled distance similar to the laboratory, using Wilcoxon paired tests and Spearman’s correlations. Most gait characteristics differed between both environments in both groups. Numerous high correlations were found between laboratory and daily life gait parameters for the CP group, whereas fewer correlations were found in the TD group. These results demonstrated that children with CP perform better in clinical settings. Such quantitative evidence may enhance clinicians’ understanding of the gap between capacity and performance in children with CP and improve their decision-making.

Protocol for the PreventIT feasibility randomised controlled trial of a lifestyle-integrated exercise intervention in young older adults

INTRODUCTION

The European population is rapidly ageing. In order to handle substantial future challenges in the healthcare system, we need to shift focus from treatment towards health promotion. The PreventIT project has adapted the Lifestyle-integrated Exercise (LiFE) programme and developed an intervention for healthy young older adults at risk of accelerated functional decline. The intervention targets balance, muscle strength and physical activity, and is delivered either via a smartphone application (enhanced LiFE, eLiFE) or by use of paper manuals (adapted LiFE, aLiFE).

METHODS AND ANALYSIS

The PreventIT study is a multicentre, three-armed feasibility randomised controlled trial, comparing eLiFE and aLiFE against a control group that receives international guidelines of physical activity. It is performed in three European cities in Norway, Germany, and The Netherlands. The primary objective is to assess the feasibility and usability of the interventions, and to assess changes in daily life function as measured by the Late-Life Function and Disability Instrument scale and a physical behaviour complexity metric. Participants are assessed at baseline, after the 6 months intervention period and at 1 year after randomisation. Men and women between 61 and 70 years of age are randomly drawn from regional registries and respondents screened for risk of functional decline to recruit and randomise 180 participants (60 participants per study arm).

ETHICS AND DISSEMINATION

Ethical approval was received at all three trial sites. Baseline results are intended to be published by late 2018, with final study findings expected in early 2019. Subgroup and further in-depth analyses will subsequently be published.

TRIAL REGISTRATION NUMBER

NCT03065088; Pre-results.

Abnormal postural behavior in patients with functional movement disorders during exposure to stress

BACKGROUND

Patients affected by functional (psychogenic) movement disorders (FMD) have abnormal processing of stress responses. However, little is known about the influence of this abnormal stress processing on automatic motor defense behavior, such as freeze response. Our aim was thus to investigate stress-induced postural motor responses in FMD.

METHODS

Nine FMD patients and thirteen healthy controls were engaged in the Trier Social Stress Test, while we measured the movement of their body by means of accelerometers and gyroscopes attached to the thorax. Standard deviation of thorax acceleration, reflecting the variability of movement amplitude (body sway), was compared across groups over time in a 2 × 2 ANOVA design. Higuchi's fractal dimension (HFD), reflecting the complexity of movement pattern over time, was also analyzed. Salivary cortisol and α-amylase samples were collected before and after the experiment, as stress biomarkers. Pearson's correlation coefficients were calculated between these biomarkers and movement parameters.

RESULTS

A significant interaction effect was found, showing that healthy controls reduced their thorax sway over time during exposure to stress (from 0.027 ± 0.010 m/s² to 0.023 ± 0.008 m/s², effect size of Cohen's d = 0.95), whereas patients with FMD did not. This change in body sway in controls over time negatively correlated with salivary cortisol values (ρ = -0.67, p = 0.012). A significant group effect revealed that FMD patients had an overall larger body sway (0.038 ± 0.013 m/s²) compared to controls (0.025 ± 0.009 m/s² - effect size of Cohen's d = 1.29) and a lower HFD (1.602 ± 0.071) than controls (1.710 ± 0.078 - Cohen's d = 1.43).

CONCLUSIONS

Patients with FMD failed to show a reduction of body sway over time, i.e., freeze response observed in the controls, thus suggesting an impairment in the automatic defense behavior. Moreover, our analysis found a lower complexity of movement (HFD) in FMD, which deserves future research in order to verify whether this could represent a characteristic trait of the disorder.

Correction to: Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions

The original article [1] contained a minor error whereby the middle initial of Christopher J. Newman's name was mistakenly omitted.

Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions

BACKGROUND

Physical therapy interventions for ambulatory youth with cerebral palsy (CP) often focus on activity-based strategies to promote functional mobility and participation in physical activity. The use of activity monitors validated for this population could help to design effective personalized interventions by providing reliable outcome measures. The objective of this study was to devise a single-sensor based algorithm for locomotion and cadence detection, robust to atypical gait patterns of children with CP in the real-life like monitoring conditions.

METHODS

Study included 15 children with CP, classified according to Gross Motor Function Classification System (GMFCS) between levels I and III, and 11 age-matched typically developing (TD). Six IMU devices were fixed on participant's trunk (chest and low back/L5), thighs, and shanks. IMUs on trunk were independently used for development of algorithm, whereas the ensemble of devices on lower limbs were used as reference system. Data was collected according to a semi-structured protocol, and included typical daily-life activities performed indoor and outdoor. The algorithm was based on detection of peaks associated to heel-strike events, identified from the norm of trunk acceleration signals, and included several processing stages such as peak enhancement and selection of the steps-related peaks using heuristic decision rules. Cadence was estimated using time- and frequency-domain approaches. Performance metrics were sensitivity, specificity, precision, error, intra-class correlation coefficient, and Bland-Altman analysis.

RESULTS

According to GMFCS, CP children were classified as GMFCS I (n = 7), GMFCS II (n = 3) and GMFCS III (n = 5). Mean values of sensitivity, specificity and precision for locomotion detection ranged between 0.93-0.98, 0.92-0.97 and 0.86-0.98 for TD, CP-GMFCS I and CP-GMFCS II-III groups, respectively. Mean values of absolute error for cadence estimation (steps/min) were similar for both methods, and ranged between 0.51-0.88, 1.18-1.33 and 1.94-2.3 for TD, CP-GMFCS I and CP-GMFCS II-III groups, respectively. The standard deviation was higher in CP-GMFCS II-III group, the lower performances being explained by the high variability of atypical gait patterns.

CONCLUSIONS

The algorithm demonstrated good performance when applied to a wide range of gait patterns, from normal to the pathological gait of highly affected children with CP using walking aids.

Concern about Falling and Complexity of Free-Living Physical Activity Patterns in Well-Functioning Older Adults

BACKGROUND

Fall-related psychological concerns are common among older adults, potentially contributing to functional decline as well as to restriction of activities and social participation. To effectively prevent such negative consequences, it is important to understand how even very low concern about falling could affect physical activity behavior in everyday life. We hypothesized that concern about falling is associated with a reduction in diversity, dynamics, and performance of daily activities, and that these features can be comprehensively quantified in terms of complexity of physical activity patterns.

METHODS

A sample of 40 community-dwelling older adults were assessed for concern about falling using the Falls Efficacy Scale-International (FES-I). Free-living physical activity was assessed using a set of metrics derived from data recorded with a chest-worn tri-axial accelerometer. The devised metrics characterized physical activity behavior in terms of endurance (total locomotion time, longest locomotion period, usual walking cadence), performance (cadence of longest locomotion period, locomotion periods with at least 30 steps and 100 steps/min), and complexity of physical activity patterns. Complexity was quantified according to variations in type, intensity, and duration of activities, and was considered as an adaptive response to environmental exigencies over the course of the day.

RESULTS

Based on FES-I score, participants were classified into two groups: not concerned at all/fully confident (n = 25) and concerned/less confident (n = 15). Demographic and health-related variables did not differ significantly between groups. Comparison of physical activity behavior indicated no significant differences for endurance-related metrics. In contrast, performance and complexity metrics were significantly lower in the less confident group compared to the fully confident group. Among all metrics, complexity of physical activity patterns appeared as the most discriminative feature between fully confident and less confident participants (p = 0.001, non-parametric Cliff's delta effect size = 0.63).

CONCLUSIONS

These results extend our understanding of the interplay between low concern about falling and physical activity behavior of community-dwelling older persons in their everyday life context. This information could serve to better design and evaluate personalized intervention programs in future prospective studies.

Classification and characterization of postural transitions using instrumented shoes

The frequency and quality of sit-to-stand and stand-to-sit postural transitions decrease with age and are highly relevant for fall risk assessment. Accurate classification and characterization of these transitions in daily life of older adults are therefore needed. In this study, we propose to use instrumented shoes for postural transition classification as well as transition duration estimation from insole force signals. In the first part, data were collected with 10 older adults and 10 young participants performing transitions in the laboratory while wearing the instrumented shoes, without arm assistance. A wavelet approach was used to transform the insole force data, and candidate events were selected for transition duration estimation. Transition durations were then validated against a model based on force plate reference. Vertical force estimation was also compared to force plate measurement. In the second part, postural transitions were classified in daily life using the instrumented shoes and validated against a highly accurate wearable system. Transition duration was estimated with an error ranging from 10 to 20% while the error for vertical force estimation was 7%. Postural transition classification was achieved with excellent sensitivity and precision exceeding 90%. In conclusion, the instrumented shoes are suitable for classifying and characterizing postural transitions in daily life conditions of healthy older adults. Graphical abstract "Experimental setup showing instrumented shoes, reference force plate, as well as IMUs used for postural transition classification and duration estimation comparison".

Quantifying dimensions of physical behavior in chronic pain conditions

Background

Chronic pain, defined as persistent or recurrent pain lasting longer than 3 months, is a frequent condition affecting an important percent of population worldwide. Pain chronicity can be caused by many different factors and is a frequent component of many neurological disorders. An important aspect for clinical assessment and design of effective treatment and/or rehabilitation strategies is to better understand the impact of pain on domains of functioning in everyday life. The aim of this study was to identify the objectively quantifiable features of physical functioning in daily life and to evaluate their effectiveness to differentiate behavior among subjects with different pain conditions.

Method

Body worn sensors were used to record movement data during five consecutive days in 92 subjects. Sensor data were processed to characterize the physical behavior in terms of type, intensity, duration and temporal pattern of activities, postures and movements performed by subjects in daily life. Metrics quantifying these features were subsequently used to devise composite scores using a factor analysis approach. The severity of clinical condition was assessed using a rating of usual pain intensity on a 10-cm visual analog scale. The relationship between pain intensity and the estimated metrics/composite scores was assessed using multiple regression and discriminant analysis.

Results

According to the factor analysis solution, two composite scores were identified, one integrating the metrics quantifying the amount and duration of activity periods, and the other the metrics quantifying complexity of temporal patterns, i.e., the diversity of body movements and activities, and the manner in which they are organized throughout time. All estimated metrics and composite scores were significantly different between groups of subjects with clinically different pain levels. Moreover, analysis revealed that pain intensity seemed to have a more significant impact on the overall physical behavior, as it was quantified by a global composite score, whereas the type of chronic pain appeared to influence mostly the complexity of the temporal pattern.

Conclusion

The methodology described could be informative for the design of objective outcome measures in chronic pain management/rehabilitation programs.

Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes

Activity level and gait parameters during daily life are important indicators for clinicians because they can provide critical insights into modifications of mobility and function over time. Wearable activity monitoring has been gaining momentum in daily life health assessment. Consequently, this study seeks to validate an algorithm for the classification of daily life activities and to provide a detailed gait analysis in older adults. A system consisting of an inertial sensor combined with a pressure sensing insole has been developed. Using an algorithm that we previously validated during a semi structured protocol, activities in 10 healthy elderly participants were recorded and compared to a wearable reference system over a 4 h recording period at home. Detailed gait parameters were calculated from inertial sensors. Dynamics of physical behavior were characterized using barcodes that express the measure of behavioral complexity. Activity classification based on the algorithm led to a 93% accuracy in classifying basic activities of daily life, i.e., sitting, standing, and walking. Gait analysis emphasizes the importance of metrics such as foot clearance in daily life assessment. Results also underline that measures of physical behavior and gait performance are complementary, especially since gait parameters were not correlated to complexity. Participants gave positive feedback regarding the use of the instrumented shoes. These results extend previous observations in showing the concurrent validity of the instrumented shoes compared to a body-worn reference system for daily-life physical behavior monitoring in older adults.

Wearable Barometric Pressure Sensor to Improve Postural Transition Recognition of Mobility-Impaired Stroke Patients

Sit-to-stand and Stand-to-sit transfers (STS) provide relevant information regarding the functional limitation of mobility-impaired patients. The characterization of STS pattern using a single trunk fixed inertial sensor has been proposed as an objective tool to assess changes in functional ability and balance due to disease. Despite significant research efforts, STS quantification remains challenging due to the high inter- and between- subject variability of this motion pattern. The present study aims to improve the performance of STS detection and classification by fusing the information from barometric pressure (BP) and inertial sensors while keeping a single sensor located at the trunk. A total number of 345 STSs were recorded from 12 post-stroke patients monitored in a semi-structured conditioned protocol. Model-based features of BP signal were combined with kinematic parameters from accelerometer and/or gyroscope and used in a logistic regression-based classifier to detect STS and then identify their types. The correct classification rate was 90.6% with full sensor (BP and inertial) configuration and 75.4% with single inertial sensor. Receiver-Operating-Characteristics analysis was carried out to characterize the robustness of the models. The results demonstrate the potential of BP sensor to improve the detection and classification of STSs when monitoring is performed unobtrusively in every-day life.

Instrumented shoes for activity classification in the elderly

Quantifying daily physical activity in older adults can provide relevant monitoring and diagnostic information about risk of fall and frailty. In this study, we introduce instrumented shoes capable of recording movement and foot loading data unobtrusively throughout the day. Recorded data were used to devise an activity classification algorithm. Ten elderly persons wore the instrumented shoe system consisting of insoles inside the shoes and inertial measurement units on the shoes, and performed a series of activities of daily life as part of a semi-structured protocol. We hypothesized that foot loading, orientation, and elevation can be used to classify postural transitions, locomotion, and walking type. Additional sensors worn at the right thigh and the trunk were used as reference, along with an event marker. An activity classification algorithm was built based on a decision tree that incorporates rules inspired from movement biomechanics. The algorithm revealed excellent performance with respect to the reference system with an overall accuracy of 97% across all activities. The algorithm was also capable of recognizing all postural transitions and locomotion periods with elevation changes. Furthermore, the algorithm proved to be robust against small changes of tuning parameters. This instrumented shoe system is suitable for daily activity monitoring in elderly persons and can additionally provide gait parameters, which, combined with activity parameters, can supply useful clinical information regarding the mobility of elderly persons.

Instrumented Shoes for Real-Time Activity Monitoring Applications

Activity monitoring in daily life is gaining momentum as a health assessment tool, especially in older adults and at-risk populations. Several research-based and commercial systems have been proposed with varying performances in classification accuracy. Configurations with many sensors are generally accurate but cumbersome, whereas single sensors tend to have lower accuracies. To this end, we propose an instrumented shoes system capable of accurate activity classification and gait analysis that contains sensors located entirely at the level of the shoes. One challenge in daily activity monitoring is providing punctual and subject-tailored feedback to improve mobility. Therefore, the instrumented shoe system was equipped with a Bluetooth® module to transmit data to a smartphone and perform detailed activity profiling of the monitored subjects. The potential applications of such a system are numerous in mobility and fall risk-assessment as well as in fall prevention.

Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients

BACKGROUND

Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients' mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor.

METHODS

Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH).

RESULTS

The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation.

CONCLUSION

The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier.

Unraveling dynamics of human physical activity patterns in chronic pain conditions

Chronic pain is a complex disabling experience that negatively affects the cognitive, affective and physical functions as well as behavior. Although the interaction between chronic pain and physical functioning is a well-accepted paradigm in clinical research, the understanding of how pain affects individuals' daily life behavior remains a challenging task. Here we develop a methodological framework allowing to objectively document disruptive pain related interferences on real-life physical activity. The results reveal that meaningful information is contained in the temporal dynamics of activity patterns and an analytical model based on the theory of bivariate point processes can be used to describe physical activity behavior. The model parameters capture the dynamic interdependence between periods and events and determine a ‘signature’ of activity pattern. The study is likely to contribute to the clinical understanding of complex pain/disease-related behaviors and establish a unified mathematical framework to quantify the complex dynamics of various human activities.

Suitability of commercial barometric pressure sensors to distinguish sitting and standing activities for wearable monitoring

Despite its medical relevance, accurate recognition of sedentary (sitting and lying) and dynamic activities (e.g. standing and walking) remains challenging using a single wearable device. Currently, trunk-worn wearable systems can differentiate sitting from standing with moderate success, as activity classifiers often rely on inertial signals at the transition period (e.g. from sitting to standing) which contains limited information. Discriminating sitting from standing thus requires additional sources of information such as elevation change. The aim of this study is to demonstrate the suitability of barometric pressure, providing an absolute estimate of elevation, for evaluating sitting and standing periods during daily activities. Three sensors were evaluated in both calm laboratory conditions and a pilot study involving seven healthy subjects performing 322 sitting and standing transitions, both indoor and outdoor, in real-world conditions. The MS5611-BA01 barometric pressure sensor (Measurement Specialties, USA) demonstrated superior performance to counterparts. It discriminates actual sitting and standing transitions from stationary postures with 99.5% accuracy and is also capable to completely dissociate Sit-to-Stand from Stand-to-Sit transitions.

Improved physical activity in patients treated for chronic pain by spinal cord stimulation

The objective of this study was to objectively assess the physical activity of daily living in chronic pain patients treated with spinal cord stimulation (SCS). Changes in pain and spontaneous physical activity following SCS were evaluated under real life conditions. Five series of measurements were performed before the implant (baseline) and at one, three, six, and 12 months after the implantation of an SCS system. Compared to baseline values, physical activity increased consistently during the entire follow-up period. The time spent walking and standing was statistically increased after six months (p < 0.01) and the time spent lying decreased significantly (p < 0.001) at the same time. The average total walking distance increased up to 389% at 12 months, reaching statistical significance (p < 0.05) after three months. The stride length and the speed increased (p < 0.01) at all times. We conclude that the reduction in pain intensity due to SCS is associated with a progressive and sustained improvement in physical activity. j.

Foot worn inertial sensors for gait assessment and rehabilitation based on motorized shoes

Fall prevention in elderly subjects is often based on training and rehabilitation programs that include mostly traditional balance and strength exercises. By applying such conventional interventions to improve gait performance and decrease fall risk, some important factors are neglected such as the dynamics of the gait and the motor learning processes. The EU project "Self Mobility Improvement in the eLderly by counteractING falls" (SMILING project) aimed to improve age-related gait and balance performance by using unpredicted external perturbations during walking through motorized shoes that change insole inclination at each stance. This paper describes the shoe-worn inertial module and the gait analysis method needed to control in real-time the shoe insole inclination during training, as well as gait spatio-temporal parameters obtained during long distance walking before and after the 8-week training program that assessed the efficacy of training with these motorized shoes.

Barcoding human physical activity to assess chronic pain conditions

BACKGROUND

Modern theories define chronic pain as a multidimensional experience - the result of complex interplay between physiological and psychological factors with significant impact on patients' physical, emotional and social functioning. The development of reliable assessment tools capable of capturing the multidimensional impact of chronic pain has challenged the medical community for decades. A number of validated tools are currently used in clinical practice however they all rely on self-reporting and are therefore inherently subjective. In this study we show that a comprehensive analysis of physical activity (PA) under real life conditions may capture behavioral aspects that may reflect physical and emotional functioning.

METHODOLOGY

PA was monitored during five consecutive days in 60 chronic pain patients and 15 pain-free healthy subjects. To analyze the various aspects of pain-related activity behaviors we defined the concept of PA 'barcoding'. The main idea was to combine different features of PA (type, intensity, duration) to define various PA states. The temporal sequence of different states was visualized as a 'barcode' which indicated that significant information about daily activity can be contained in the amount and variety of PA states, and in the temporal structure of sequence. This information was quantified using complementary measures such as structural complexity metrics (information and sample entropy, Lempel-Ziv complexity), time spent in PA states, and two composite scores, which integrate all measures. The reliability of these measures to characterize chronic pain conditions was assessed by comparing groups of subjects with clinically different pain intensity.

CONCLUSION

The defined measures of PA showed good discriminative features. The results suggest that significant information about pain-related functional limitations is captured by the structural complexity of PA barcodes, which decreases when the intensity of pain increases. We conclude that a comprehensive analysis of daily-life PA can provide an objective appraisal of the intensity of pain.

Gait assessment in children with duchenne muscular dystrophy during long-distance walking

The aim of this study was to investigate the alteration of the gait pattern in 25 children with Duchenne muscular dystrophy, using body-worn inertial sensors during a long walking distance. Normalized spatiotemporal gait parameters and their variability were extracted from the angular velocity of the shanks; the smoothness of the trunk movement was assessed based on the spectral entropy of the acceleration norm. As compared to healthy children, patients with Duchenne muscular dystrophy showed significantly lower stride velocity and a less smooth trunk movement. When the group of patients was divided into mild and moderate based on the Motor Function Measure, the authors noticed significantly higher values both for cadence and stride velocity, as well as improved trunk smoothness in the mild versus moderate group. The potential of such parameters to distinguish between different disease states opens new perspectives for the objective assessment of efficacy of the new therapies associated with Duchenne muscular dystrophy.

Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people

The aim of this study was to extract multi-parametric measures characterizing different features of sit-to-stand (Si-St) and stand-to-sit (St-Si) transitions in older persons, using a single inertial sensor attached to the chest. Investigated parameters were transition's duration, range of trunk tilt, smoothness of transition pattern assessed by its fractal dimension, and trunk movement's dynamic described by local wavelet energy. A measurement protocol with a Si-St followed by a St-Si postural transition was performed by two groups of participants: the first group (N=79) included Frail Elderly subjects admitted to a post-acute rehabilitation facility and the second group (N=27) were healthy community-dwelling elderly persons. Subjects were also evaluated with Tinetti's POMA scale. Compared to Healthy Elderly persons, frail group at baseline had significantly longer Si-St (3.85±1.04 vs. 2.60±0.32, p=0.001) and St-Si (4.08±1.21 vs. 2.81±0.36, p=0.001) transition's duration. Frail older persons also had significantly decreased smoothness of Si-St transition pattern (1.36±0.07 vs. 1.21±0.05, p=0.001) and dynamic of trunk movement. Measurements after three weeks of rehabilitation in frail older persons showed that smoothness of transition pattern had the highest improvement effect size (0.4) and discriminative performance. These results demonstrate the potential interest of such parameters to distinguish older subjects with different functional and health conditions.