Instrumental Assessment of Stair Ascent in People With Multiple Sclerosis, Stroke, and Parkinson's Disease: A Wearable-Sensor-Based Approach

Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

BACKGROUND

Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains.

OBJECTIVE

This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades.

METHODS

We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines.

RESULTS

Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good.

CONCLUSIONS

Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS.

TRIAL REGISTRATION

PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.

Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors

Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson's disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI's minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.

Clinical utility of the Trendelenburg Test in people with multiple sclerosis

BACKGROUND

The clinical utility of the Trendelenburg Test remains unknown in people with multiple sclerosis (MS).

OBJECTIVE

To measure (1) intra-rater reliability, (2) agreement of goniometer-assessed Trendelenburg pelvis-on-femur angle (POF) with motion capture, and (3) concurrent validity of Trendelenburg POF and hip abduction strength with POF during walking and step negotiation.

METHODS

Trendelenburg POF was measured in 20 people with MS using goniometry and motion analysis. In addition, peak POF was measured using motion analysis during walking, step ascent, and step descent. Intra-rater reliability of goniometer-assessed Trendelenburg POF and agreement with motion analysis-assessed POF were analyzed. Pearson's r was used to determine the relationships between Trendelenburg POF and hip abduction strength with peak POF during each functional activity.

RESULTS

Goniometer-assessed Trendelenburg POF demonstrated very strong reliability (ICC: 0.948), strong agreement with 3D motion analysis (ICC: 0.792), correlated moderately with peak POF during walking (r = 0.519) and step ascent (r = 0.572), and weakly with step descent (r = 0.463). Hip abductor strength correlated weakly with peak POF during step ascent (r = -0.307) and negligibly during walking (r = -0.270) and step descent (r = -0.249).

CONCLUSIONS

Goniometer-assessed Trendelenburg POF was reliable, agreed with motion analysis, and may provide insight into hip abduction muscle performance during functional activities in people with MS.

Association of 7-Day Profiles of Motor Activity in Marital Dyads with One Component Affected by Parkinson's Disease

(1) Background: A noticeable association between the motor activity (MA) profiles of persons living together has been found in previous studies. Social actigraphy methods have shown that this association, in marital dyads composed of healthy individuals, is greater than that of a single person compared to itself. This study aims at verifying the association of MA profiles in dyads where one component is affected by Parkinson's disease (PD). (2) Methods: Using a wearable sensor-based social actigraphy approach, we continuously monitored, for 7 days, the activities of 27 marital dyads including one component with PD. (3) Results: The association of motor activity profiles within a marital dyad (cross-correlation coefficient 0.344) is comparable to the association of any participant with themselves (0.325). However, when considering the disease severity quantified by the UPDRS III score, it turns out that the less severe the symptoms, the more associated are the MA profiles. (4) Conclusions: Our findings suggest that PD treatment could be improved by leveraging the MA of the healthy spouse, thus promoting lifestyles also beneficial for the component affected by PD. The actigraphy approach provided valuable information on habitual functions and motor fluctuations, and could be useful in investigating the response to treatment.

Balance Impairments in People with Early-Stage Multiple Sclerosis: Boosting the Integration of Instrumented Assessment in Clinical Practice

The balance of people with multiple sclerosis (PwMS) is commonly assessed during neurological examinations through clinical Romberg and tandem gait tests that are often not sensitive enough to unravel subtle deficits in early-stage PwMS. Inertial sensors (IMUs) could overcome this drawback. Nevertheless, IMUs are not yet fully integrated into clinical practice due to issues including the difficulty to understand/interpret the big number of parameters provided and the lack of cut-off values to identify possible abnormalities. In an attempt to overcome these limitations, an instrumented modified Romberg test (ImRomberg: standing on foam with eyes closed while wearing an IMU on the trunk) was administered to 81 early-stage PwMS and 38 healthy subjects (HS). To facilitate clinical interpretation, 21 IMU-based parameters were computed and reduced through principal component analysis into two components, sway complexity and sway intensity, descriptive of independent aspects of balance, presenting a clear clinical meaning and significant correlations with at least one clinical scale. Compared to HS, early-stage PwMS showed a 228% reduction in sway complexity and a 63% increase in sway intensity, indicating, respectively, a less automatic (more conscious) balance control and larger and faster trunk movements during upright posture. Cut-off values were derived to identify the presence of balance abnormalities and if these abnormalities are clinically meaningful. By applying these thresholds and integrating the ImRomberg test with the clinical tandem gait test, balance impairments were identified in 58% of PwMS versus the 17% detected by traditional Romberg and tandem gait tests. The higher sensitivity of the proposed approach would allow for the direct identification of early-stage PwMS who could benefit from preventive rehabilitation interventions aimed at slowing MS-related functional decline during neurological examinations and with minimal modifications to the tests commonly performed.

Wearable-Based Stair Climb Power Estimation and Activity Classification

Stair climb power (SCP) is a clinical measure of leg muscular function assessed in-clinic via the Stair Climb Power Test (SCPT). This method is subject to human error and cannot provide continuous remote monitoring. Continuous monitoring using wearable sensors may provide a more comprehensive assessment of lower-limb muscular function. In this work, we propose an algorithm to classify stair climbing periods and estimate SCP from a lower-back worn accelerometer, which strongly agrees with the clinical standard (r = 0.92, p < 0.001; ICC = 0.90, [0.82, 0.94]). Data were collected in-lab from healthy adults (n = 65) performing the four-step SCPT and a walking assessment while instrumented (accelerometer + gyroscope), which allowed us to investigate tradeoffs between sensor modalities. Using two classifiers, we were able to identify periods of stair ascent with >89% accuracy [sensitivity = >0.89, specificity = >0.90] using two ensemble machine learning algorithms, trained on accelerometer signal features. Minimal changes in model performances were observed using the gyroscope alone (±0−6% accuracy) versus the accelerometer model. While we observed a slight increase in accuracy when combining gyroscope and accelerometer (about +3−6% accuracy), this is tolerable to preserve battery life in the at-home environment. This work is impactful as it shows potential for an accelerometer-based at-home assessment of SCP.

An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks

There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient (ICC2,1)] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (&lt;10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found (ICC2,1=0.817− 0.999). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.

Gait Classification with Gait Inherent Attribute Identification from Ankle's Kinematics

The human ankle joint interacts with the environment during ambulation to provide mobility and maintain stability. This association changes depending on the different gait patterns of day-to-day life. In this study, we investigated this interaction and extracted kinematic information to classify human walking mode into upstairs, downstairs, treadmill, overground and stationary in real-time using a single-DoF IMU axis. The proposed algorithm's uniqueness is twofold - it encompasses components of the ankle's biomechanics and subject-specificity through the extraction of inherent walking attributes and user calibration. The performance analysis with forty healthy participants (mean age: 26.8 ± 5.6 years yielded an accuracy of 89.57% and 87.55% in the left and right sensors, respectively. The study, also, portrays the implementation of heuristics to combine predictions from sensors at both feet to yield a single conclusive decision with better performance measures. The simplicity yet reliability of the algorithm in healthy participants and the observation of inherent multimodal walking features, similar to young adults, in elderly participants through a case study, demonstrate our proposed algorithm's potential as a high-level automatic switching framework in robotic gait interventions for multimodal walking.

Walking With Horizontal Head Turns Is Impaired in Persons With Early-Stage Multiple Sclerosis Showing Normal Locomotion

Background

Turning the head while walking (an action often required during daily living) is particularly challenging to maintain balance. It can therefore potentially reveal subtle impairments in early-stage people with multiple sclerosis who still show normal locomotion (NW-PwMS). This would help in identifying those subjects who can benefit from early preventive exercise aimed at slowing the MS-related functional decline.

Objectives

To analyze if the assessment of walking with horizontal head turns (WHHT) through inertial sensors can discriminate between healthy subjects (HS) and NW-PwMS and between NW-PwMS subgroups. To assess if the discriminant ability of the instrumented WHHT is higher compared to clinical scores. To assess the concurrent validity of the sensor-based metrics.

Methods

In this multicenter study, 40 HS and 59 NW-PwMS [Expanded Disability Status Scale (EDSS) ≤ 2.5, disease duration ≤ 5 years] were tested. Participants executed Item-6 of the Fullerton Advanced Balance scale-short (FAB-s) wearing three inertial sensors on the trunk and ankles. The item required to horizontally turn the head at a beat of the metronome (100 bpm) while walking. Signals of the sensors were processed to compute spatiotemporal, regularity, symmetry, dynamic stability, and trunk sway metrics descriptive of WHHT.

Results

Mediolateral regularity, anteroposterior symmetry, and mediolateral stability were reduced in NW-PwMS vs. HS (p ≤ 0.001), and showed moderate discriminant ability (area under the receiver operator characteristic curve [AUC]: 0.71–0.73). AP symmetry and ML stability were reduced (p ≤ 0.026) in EDSS: 2–2.5 vs. EDSS: 0–1.5 subgroup (AUC: 0.69–0.70). The number of NW-PwMS showing at least one abnormal instrumented metric (68%) was larger (p ≤ 0.002) than the number of participants showing abnormal FAB-s-Item6 (32%) and FAB-s clinical scores (39%). EDSS: 2–2.5 subgroup included more individuals showing abnormal instrumented metrics (86%) compared to EDSS: 0–1.5 subgroup (57%). The instrumented metrics significantly correlated with FAB-s-Item6 and FAB-s scores (|Spearman's r_s| ≥ 0.37, p < 0.001), thus demonstrating their concurrent validity.

Conclusion

The instrumented assessment of WHHT provided valid objective metrics that discriminated, with higher sensitivity than clinical scores, between HS and NW-PwMS and between EDSS subgroups. The method is a promising tool to complement clinical evaluation, and reveal subclinical impairments in persons who can benefit from early preventive rehabilitative interventions.

Wearable technologies to measure clinical outcomes in multiple sclerosis: A scoping review

Wearable technology refers to any sensor worn on the person, making continuous and remote monitoring available to many people with chronic disease, including multiple sclerosis (MS). Daily monitoring seems an ideal solution either as an outcome measure or as an adjunct to support rater-based monitoring in both clinical and research settings. There has been an increase in solutions that are available, yet there is little consensus on the most appropriate solution to use in either MS research or clinical practice. We completed a scoping review (using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines) to summarise the wearable solutions available in MS, to identify those approaches that could potentially be utilised in clinical trials, by evaluating the following: scalability, cost, patient adaptability and accuracy. We identified 35 unique products that measure gait, cognition, upper limb function, activity, mood and fatigue, with most of these solutions being phone applications.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

Movement compensations during a step ascent task are associated with stair climbing performance in people with multiple sclerosis

BACKGROUND

The biomechanical mechanisms underlying stair climbing limitations are poorly understood in people with multiple sclerosis (MS).

RESEARCH QUESTIONS

Are trunk and pelvis motion and lower extremity joint moments during step ascent different between MS and control groups? Are step ascent biomechanics and stair climbing performance associated in people with MS?

METHODS

20 people with MS (49 ± 12 years, EDSS range: 1.5-5.5) and ten control participants (48 ± 12 years) underwent three-dimensional motion analysis while ascending a 15.2-cm step and also completed a timed Functional Stair Test. Main effects of group (MS vs Control) and limb (Stronger/Dominant vs Weaker/Non-dominant) and interactions were assessed using two-way analyses of variance. Associations between movement patterns during the step ascent and Functional Stair Test performance were performed using Pearson's correlations and backward stepwise linear regression.

RESULTS

Significant group main effects were observed in greater sagittal pelvis excursion (p < 0.001), greater sagittal (p = 0.013) and frontal (p = 0.001) trunk excursion, and lower trail limb peak ankle plantar flexion moment (p < 0.001) of the MS group. Significant limb main effects were observed with greater sagittal trunk excursion (p = 0.037) and peak trail limb ankle plantar flexion moment (p = 0.037) in the stronger/dominant limb. A significant interaction was observed in peak knee extensor moment (p = .002). Stair climbing performance in the MS group correlated with sagittal (r = .607, p=<0.001) and frontal pelvis excursions (r = 0.385, p = 0.014), sagittal trunk excursion (r = .411, p = 0.008), and ankle plantar flexion moments (r=-0.415, p = 0.008). Sagittal and frontal pelvis excursion and bilateral handrail use explained a significant amount of variability in stair climbing performance (Adj R² = 0.775).

SIGNIFICANCE

In conclusion, despite the presence of proximal and distal lower extremity movement pattern compensations during a step ascent task, larger pelvis angular excursions are associated with impaired stair climbing performance in people with MS and may serve as targets for future rehabilitation interventions.

Instrumentally assessed gait quality is more relevant than gait endurance and velocity to explain patient-reported walking ability in early-stage multiple sclerosis

BACKGROUND AND PURPOSE

People with multiple sclerosis (PwMS) often report walking limitations even when the gold standard Expanded Disability Status Scale (EDSS) indicates normal walking endurance/autonomy. The present multicenter study on early-stage PwMS aims at analyzing which aspects are associated with patient-reported walking limitations measured with the 12-item Multiple Sclerosis Walking Scale (MSWS-12).

METHODS

Eighty-two PwMS (EDSS ≤ 2.5) were assessed using the Fullerton Advanced Balance Scale-short (FAB-s), the Fatigue Severity Scale (FSS) and the 6-min Walk Test (6MWT), the latter administered also to 21 healthy subjects. Participants performed the 6MWT wearing three inertial sensors on ankles and trunk. Instrumented metrics describing gait velocity (stride length and frequency) and quality (regularity, symmetry, instability) were computed from sensor data. Fatigue (FSS), balance (FAB-s), walking endurance (6MWT) and instrumented metrics were entered in a multiple regression model with MSWS-12 as dependent variable.

RESULTS

Gait symmetry, gait instability, fatigue and balance were significantly associated with self-rated walking ability, whilst walking endurance and velocity were not. Fatigue, balance, gait symmetry and instability were more impaired in participants reporting mild-to-moderate (MS_MM-PWL , 25 ≤ MSWS-12 < 75) compared to those reporting none-to-minimal (MS_nm-PWL , 0 ≤ MSWS-12 ≤ 25) perceived walking limitations. Compared to healthy subjects, gait symmetry and stability were reduced in MS_nm-PWL and MS_MM-PWL , even in those participants with EDSS ≤ 1.5.

CONCLUSION

Instrumentally assessed gait quality aspects (symmetry and instability) are associated with patient-reported walking ability in early-stage PwMS and seem sensitive biomarkers to detect subtle impairments even in the earliest stages of the disease (EDSS ≤ 1.5). Future studies should assess their ability to follow walking change due to MS progression or pharmacological/rehabilitation interventions.

Improved Gait of Persons With Multiple Sclerosis After Rehabilitation: Effects on Lower Limb Muscle Synergies, Push-Off, and Toe-Clearance

Introduction: Persons with MS (PwMS) have markedly reduced push-off and toe-clearance during gait compared to healthy subjects (HS). These deficits may result from alterations in neuromotor control at the ankle. To optimize rehabilitation interventions for PwMS, a crucial step is to evaluate if and how altered neuromotor control, as represented by muscle synergies, improves with rehabilitation. In this study we investigated changes in ankle motor control and associated biomechanical parameters during gait in PwMS, occurring with increase in speed after gait rehabilitation. Methods: 3D motion and EMG data were collected while 11 PwMS (age 50.3 + 11.1; EDSS 5.2 + 1.2) walked overground at self-selected speed before (T0) and after 20 sessions (T1) of intensive treadmill training. Muscle synergies were extracted using non-negative matrix factorization. Gait parameters were computed according to the LAMB protocol. Pearson's correlation coefficient was used to evaluate the similarity of motor modules between PwMS and HS. To assess differences in distal module activations representing neuromotor control at the ankle [Forward Propulsion (FPM) and Ground Clearance modules (GCM)], each module's activation timing was integrated over 100% of the gait cycle and the activation percentage index (API) was computed in six phases. Ten age matched HS provided two separate speed-matched normative datasets for T0 and T1. For speed independent comparison for the PwMs Z scores were calculated for all their gait variables. Results: In PwMS velocity increased significantly from T0 to T1 (0.74-0.90 m/s, p < 0.05). The activation profiles (API) of FPM and GCM of PwMS improved in pre-swing (p < 0.05): FPM (Mean [95% CI] [%]: T0: 12.5 [5.7-19.3] vs. T1: 9.0 [2.7-15.3]); GCM (T0: 26.7 [18.2-35.3] vs. T1: 24.5 [18.2-30.7]). This was associated with an increase in toe clearance (80.3 to 103.6 mm, p < 0.05) and a higher ankle power peak in pre-swing (1.53-1.93 W/kg, p < 0.05). Conclusion: Increased gait speed of PwMS after intensive gait training was consistent with improvements in spatio-temporal gait parameters. The most important finding of this study was the re-organization of distal leg modules related to neurophysiological changes induced by rehabilitation. This was associated with an improved ankle performance.

Transitional Locomotor Tasks in People With Mild to Moderate Parkinson's Disease

Background: People with Parkinson's disease (PD) exhibit deficits in maintaining balance both during quiet standing and during walking, turning, standing up from sitting, and step initiation. Objective: The purpose of this study was to examine balance disorders during a transitional task under different conditions in participants with PD. Methods: The research was conducted on 15 PD-II (mild) and 15 PD-III (moderate) individuals (H&Y II-III stage) and 30 healthy elderly. The transitional task was measured on two force platforms (A and B). The procedure consisted of three phases: (1) quiet standing on platform A, (2) crossing to platform B, and (3) quiet standing on platform B, each until measurements were completed. There were four conditions: crossing without an obstacle, crossing with an obstacle, and walking up and down the step. Results: There were no significant differences between mild PD individuals and healthy elderly during quiet standing before the transitional task and after completing the task. The temporal aspects describing the different transitional tasks were comparable between mild PD and healthy subjects. Moderate PD participants presented a significantly higher COP velocity after the transitional task compared to the healthy older adults (p < 0.05). Additionally, the moderate PD group showed significantly higher values for transit time relative to healthy subjects during the transitional task in all conditions (p < 0.05). Conclusions: Disease severity affects the temporal aspects of different transitional tasks in people with PD. The procedure of completing a transitional task under different conditions allowed differences between moderate and mild PD stages and healthy subjects to be observed.

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB.

Objective sensor-based gait measures reflect motor impairment in multiple sclerosis patients: Reliability and clinical validation of a wearable sensor device

BACKGROUND

Gait deficits are common in multiple sclerosis (MS) and contribute to disability but may not be easily detected in the early stages of the disease.

OBJECTIVES

We investigated whether sensor-based gait analysis is able to detect gait impairments in patients with MS (PwMS).

METHODS

A foot-worn sensor-based gait analysis system was used in 102 PwMS and 22 healthy controls (HC) that were asked to perform the 25-foot walking test (25FWT) two times in a self-selected speed (25FWT_pref), followed by two times in a speed as fast as possible (25FWT_fast). The Multiple Sclerosis Walking Scale (MSWS-12) was used as a subjective measure of patient mobility. Patients were divided into EDSS and functional system subgroups.

RESULTS

Datasets between two consecutive measurements (test-retest-reliability) were highly correlated in all analysed mean gait parameters (e.g. 25FWT_fast: stride length r = 0.955, gait speed r = 0.969) Subgroup analysis between HC and PwMS with lower (EDSS≤3.5) and higher (EDSS 4.0-7.0) disability showed significant differences in mean stride length, gait speed, toe off angle, stance time and swing time (e.g. stride length of EDSS subgroups 25FWT_fast p ≤ 0.001, 25FWT_pref p = 0.003). The differences between EDSS subgroups were more pronounced in fast than in self-selected gait speed (e.g. stride length 25FWT_fast 33.6 cm vs. 25FWT_pref 16.3 cm). Stride length (25FWT_fast) highly correlated to EDSS (r=-0.583) and MSWS-12 (r=-0.668). We observed significant differences between HC and PwMS with (FS 0-1) and without (FS≥2) pyramidal or cerebellar disability (e.g. gait speed of FS subgroups p ≤ 0.001).

CONCLUSION

Sensor-based gait analysis objectively supports the clinical assessment of gait abnormalities even in the lower stages of MS, especially when walking with fast speed. Stride length and gait speed where identified as the most clinically relevant gait measures. Thus, it may be used to support the assessment of PwMS with gait impairment in the future, e.g. for more objective classification of disability. Its role in home-monitoring scenarios need to be evaluated in further studies.

Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis

Inertial measurement units (IMUs) allow accurate quantification of gait impairment of people with multiple sclerosis (pwMS). Nonetheless, it is not clear how IMU-based metrics might be influenced by pragmatic aspects associated with clinical translation of this approach, such as data collection settings and gait protocols. In this study, we hypothesised that these aspects do not significantly alter those characteristics of gait that are more related to quality and energetic efficiency and are quantifiable via acceleration related metrics, such as intensity, smoothness, stability, symmetry, and regularity. To test this hypothesis, we compared 33 IMU-based metrics extracted from data, retrospectively collected by two independent centres on two matched cohorts of pwMS. As a worst-case scenario, a walking test was performed in the two centres at a different speed along corridors of different lengths, using different IMU systems, which were also positioned differently. The results showed that the majority of the temporal metrics (9 out of 12) exhibited significant between-centre differences. Conversely, the between-centre differences in the gait quality metrics were small and comparable to those associated with a test-retest analysis under equivalent conditions. Therefore, the gait quality metrics are promising candidates for reliable multi-centric studies aiming at assessing rehabilitation interventions within a routine clinical context.

Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks

This paper reports the kinematic, kinetic and electromyographic (EMG) dataset of human locomotion during level walking at different velocities, toe- and heel-walking, stairs ascending and descending. A sample of 50 healthy subjects, with an age between 6 and 72 years, is included. For each task, both raw data and computed variables are reported including: the 3D coordinates of external markers, the joint angles of lower limb in the sagittal, transversal and horizontal anatomical planes, the ground reaction forces and torques, the center of pressure, the lower limb joint mechanical moments and power, the displacement of the whole body center of mass, and the surface EMG signals of the main lower limb muscles. The data reported in the present study, acquired from subjects with different ages, represents a valuable dataset useful for future studies on locomotor function in humans, particularly as normative reference to analyze pathological gait, to test the performance of simulation models of bipedal locomotion, and to develop control algorithms for bipedal robots or active lower limb exoskeletons for rehabilitation.

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use of microelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities.