Hilbert–Huang-Based Tremor Removal to Assess Postural Properties From Accelerometers

An Incremental Broad-Learning-System-Based Approach for Tremor Attenuation for Robot Tele-Operation

The existence of the physiological tremor of the human hand significantly affects the application of tele-operation systems in performing high-precision tasks, such as tele-surgery, and currently, the process of effectively eliminating the physiological tremor has been an important yet challenging research topic in the tele-operation robot field. Some scholars propose using deep learning algorithms to solve this problem, but a large number of hyperparameters lead to a slow training speed. Later, the support-vector-machine-based methods have been applied to solve the problem, thereby effectively canceling tremors. However, these methods may lose the prediction accuracy, because learning energy cannot be accurately assigned. Therefore, in this paper, we propose a broad-learning-system-based tremor filter, which integrates a series of incremental learning algorithms to achieve fast remodeling and reach the desired performance. Note that the broad-learning-system-based filter has a fast learning rate while ensuring the accuracy due to its simple and novel network structure. Unlike other algorithms, it uses incremental learning algorithms to constantly update network parameters during training, and it stops learning when the error converges to zero. By focusing on the control performance of the slave robot, a sliding mode control approach has been used to improve the performance of closed-loop systems. In simulation experiments, the results demonstrated the feasibility of our proposed method.

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.

A non-contact system for intraoperative quantitative assessment of bradykinesia in deep brain stimulation surgery

BACKGROUND AND OBJECTIVE

Deep brain stimulation (DBS) is an effective treatment for a number of neurological diseases, especially for the advanced stage of Parkinson's disease (PD). Objective assessment of patients' motor symptoms is crucial for accurate electrode targeting and treatment. Existing approaches suffer from subjective variability or interference with voluntary motion. This work is aimed to establish an objective assessment system to quantify bradykinesia in DBS surgery.

METHODS

Based on the analysis of the requirements for intraoperative assessment, we developed a system with non-contact measurement, online movement feature extraction, and interactive data analysis and visualization. An optical sensor, Leap Motion Controller (LMC), was taken to detect hand movement in three clinical tasks. A graphic user interface was designed to process, compare and visualize the collected data and assessment results online. Quantified movement features include amplitude, frequency, velocity, their decrement and variability, etc. Technical validation of the system was performed with a motion capture system (Mocap), with respect to data-level and feature-level accuracy and reliability. Clinical validation was conducted with 20 PD patients for intraoperative assessments in DBS surgery. Treatment responses with respect to the bradykinesia movement features were analyzed. Single case analysis and group statistical analysis were performed to examine the differences between preoperative and intraoperative performance, and the correlation between the clinical ratings and the quantified assessment was analyzed.

RESULTS

For the movements measured by LMC and Mocap, the average Pearson's correlation coefficient was 0.986, and the mean amplitude difference was 2.11 mm. No significant difference was found for all movement features quantified by LMC and Mocap. For the clinical tests, key movement features showed significant differences between the preoperative baseline and intraoperative performance when the brain stimulation was ON. The assessment results were significantly correlated with the MDS-UPDRS clinical ratings.

CONCLUSIONS

The proposed non-contact system has established itself as an objective intraoperative assessment, analysis, and visualization tool for DBS treatment of Parkinson's disease.

Design and Preliminary Performance Assessment of a Wearable Tremor Suppression Glove

OBJECTIVE

Approximately 25% of individualsliving with parkinsonian tremor do not respond to traditional treatments. Wearable tremor suppression devices (WTSD) provide an alternative approach, however, tremor in the fingers has not been given as much attention as tremor in the elbow and the wrist. Therefore, the objective of this study is to design a wearable tremor suppression glove that can suppress tremor simultaneously, but independently, in multiple hand joints without restricting the user's voluntary motion.

METHODS

A WTSD was designed for managing tremor in the index finger metacarpophalangeal (MCP) joint, thumb MCP joint, and the wrist. The prototype was tested and assessed on a participant living with parkinsonian tremor.

RESULTS

The experimental evaluation showed an overall suppression of 73.1%, 80.7%, and 85.5% in resting tremor, 70.2%, 79.5%, and 81% in postural tremor, and 60.0%, 58.7%, and 65.0% in kinetic tremor in the index finger MCP joint, the thumb MCP joint, and the wrist, respectively.

CONCLUSION

This first assessment of a WTSD for people living with Parkinson's disease provides confirmation of the feasibility of the approach. The next step requires a comprehensive validation on a broader population in order to evaluate the performance of the WTSD.

SIGNIFICANCE

This study demonstrates the feasibility of using a WTSD to manage hand and finger tremor. The device enriches the field of upper-limb tremor management, as the first WTSD for multiple joints of the hand.

Balance Deficits due to Cerebellar Ataxia: A Machine Learning and Cloud-Based Approach

Cerebellar ataxia (CA) refers to the disordered movement that occurs when the cerebellum is injured or affected by disease. It manifests as uncoordinated movement of the limbs, speech, and balance. This study is aimed at the formation of a simple, objective framework for the quantitative assessment of CA based on motion data. We adopted the Recurrence Quantification Analysis concept in identifying features of significance for the diagnosis. Eighty-six subjects were observed undertaking three standard neurological tests (Romberg's, Heel-shin and Truncal ataxia) to capture 213 time series inertial measurements each. The feature selection was based on engaging six different common techniques to distinguish feature subset for diagnosis and severity assessment separately. The Gaussian Naive Bayes classifier performed best in diagnosing CA with an average double cross-validation accuracy, sensitivity, and specificity of 88.24%, 85.89%, and 92.31%, respectively. Regarding severity assessment, the voting regression model exhibited a significant correlation (0.72 Pearson) with the clinical scores in the case of the Romberg's test. The Heel-shin and Truncal tests were considered for diagnosis and assessment of severity concerning subjects who were unable to stand. The underlying approach proposes a reliable, comprehensive framework for the assessment of postural stability due to cerebellar dysfunction using a single inertial measurement unit.

Evaluation of Wearable Sensor Devices in Parkinson's Disease: A Review of Current Status and Future Prospects

Parkinson's disease (PD) decreases the quality of life of the affected individuals. The incidence of PD is expected to increase given the growing aging population. Motor symptoms associated with PD render the patients unable to self-care and function properly. Given that several drugs have been developed to control motor symptoms, highly sensitive scales for clinical evaluation of drug efficacy are needed. Among such scales, the objective and continuous evaluation of wearable devices is increasingly utilized by clinicians and patients. Several electronic technologies have revolutionized the clinical monitoring of PD development, especially its motor symptoms. Here, we review and discuss the recent advances in the development of wearable devices for bradykinesia, tremor, gait, and myotonia. Our aim is to capture the experiences of patients and clinicians, as well as expand our understanding on the application of wearable technology. In so-doing, we lay the foundation for further research into the use of wearable technology in the management of PD.

Parkinson's disease and wearable devices, new perspectives for a public health issue: an integrative literature review

Parkinson's disease is the second most common neurodegenerative disease, with an estimated prevalence of 41/100,000 individuals affected aged between 40 and 49 years old and 1,900/100,000 aged 80 and over. Based on the essentiality of ascertaining which wearable devices have clinical literary evidence and with the purpose of analyzing the information revealed by such technologies, we conducted this scientific article of integrative review. It is an integrative review, whose main objective is to carry out a summary of the state of the art of wearable devices used in patients with Parkinson's disease. After the review, we retrieved 8 papers. Of the selected articles, only 3 were not systematic reviews; one was a series of cases and two prospective longitudinal studies. These technologies have a very rich field of application; however, research is still necessary to make such evaluations reliable and crucial to the well-being of these patients.

A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson's Disease

Background

Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity.

Methods

In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution.

Results

Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy).

Conclusions

The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review

Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen's kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., "active aging", biofeedback-based rehabilitation for fall prevention, and the management of Parkinson's disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology.

OBJECTIVES

This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring).

DATA SOURCES

The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016.

STUDY ELIGIBILITY CRITERIA

Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers.

RESULTS

Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

Potential of APDM mobility lab for the monitoring of the progression of Parkinson's disease

APDM's Mobility Lab system provides portable, validated, reliable, objective measures of balance and gait that are sensitive to Parkinson's disease (PD). In this review, we describe the potential of objective measures collected with the Mobility Lab system for tracking longitudinal progression of PD. Balance and gait are among the most important motor impairments influencing quality of life for people with PD. Mobility Lab uses body-worn, Opal sensors on the legs, trunk and arms during prescribed tasks, such as the instrumented Get Up and Go test or quiet stance, to quickly quantify the quality of balance and gait in the clinical environment. The same Opal sensors can be sent home with patients to continuously monitor the quality of their daily activities. Objective measures have the potential to monitor progression of mobility impairments in PD throughout its course to improve patient care and accelerate clinical trials.

Unsupervised home monitoring of Parkinson's disease motor symptoms using body-worn accelerometers

INTRODUCTION

Current PD assessment methods have inherent limitations. There is need for an objective method to assist clinical decisions and to facilitate evaluation of treatments. Accelerometers, and analysis using artificial neural networks (ANN), have shown potential as a method of motor symptom evaluation. This work describes the development of a novel PD disease state detection system informed by algorithms based on data collected in an unsupervised, home environment. We evaluated whether this approach can reproduce patient-completed symptom diaries and clinical assessment of disease state.

METHODS

34 participants with PD wore bilateral wrist-worn accelerometers for 4 h in a research facility (phase 1) and for 7 days at home whilst completing symptom diaries (phase 2). An ANN to predict disease state was developed based on home-derived accelerometer data. Using a leave-one-out approach, ANN performance was evaluated against patient-completed symptom diaries and against clinician rating of disease state.

RESULTS

In the clinical setting, specificity for dyskinesia detection was extremely high (0.99); high specificity was also demonstrated for home-derived data (0.93), but with low sensitivity (0.38). In both settings, sensitivity for on/off detection was sub-optimal. ANN-derived values of the proportions of time in each disease state showed strong, significant correlations with patient-completed symptom diaries.

CONCLUSION

Accurate, real-time evaluation of symptoms in an unsupervised, home environment, with this sensor system, is not yet achievable. In terms of the amounts of time spent in each disease state, ANN-derived results were comparable to those of symptom diaries, suggesting this method may provide a valuable outcome measure for medication trials.

Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson's Disease

A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

Hilbert-Huang transform based instrumental assessment of intention tremor in multiple sclerosis

OBJECTIVE

This paper describes a method to extract upper limb intention tremor from gyroscope data, through the Hilbert-Huang transform (HHT), a technique suitable for the study of nonlinear and non-stationary processes. The aims of the study were to: (i) evaluate the method's ability to discriminate between healthy controls and MS subjects; (ii) validate the proposed procedure against clinical tremor scores assigned using Fahn's tremor rating scale (FTRS); and (iii) compare the performance of the HHT-based method with that of linear band-pass filters.

APPROACH

HHT was applied on gyroscope data collected on 20 MS subjects and 13 healthy controls (CO) during finger-to-nose tests (FNTs) instrumented with an inertial sensor placed on the hand. The results were compared to those obtained after traditional linear filtering. The tremor amplitude was quantified with instrumental indexes (TIs) and clinical FTRS ratings.

MAIN RESULTS

The TIs computed after HHT-based filtering discriminated between CO and MS subjects with clinically-detected intention tremor (MS_T). In particular, TIs were significantly higher in the final part of the movement (TI2) with respect to the first part (TI1), and, for all components (X, Y, Z), MS_T showed a TI2 significantly higher than in CO subjects. Moreover, the HHT detected subtle alterations not visible from clinical ratings, as TI2 (Z-component) was significantly increased in MS subjects without clinically-detected tremor (MS_NT). The method's validity was demonstrated by significant correlations between clinical FTRS scores and TI2 related to X (rs = 0.587, p = 0.006) and Y (rs = 0.682, p < 0.001) components. Contrarily, fewer differences among the groups and no correlation between instrumental and clinical indexes emerged after traditional filtering.

SIGNIFICANCE

The present results supported the use of the HHT-based procedure for a fully-automated quantitative and objective measure of intention tremor in MS, which can overcome the limitations of clinical scales and provide supplementary information about this sign.

MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy

Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users’ health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson’s disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

Speaker Identification Using Empirical Mode Decomposition-Based Voice Activity Detection Algorithm under Realistic Conditions

Speaker recognition (SR) under mismatched conditions is a challenging task. Speech signal is nonlinear and nonstationary, and therefore, difficult to analyze under realistic conditions. Also, in real conditions, the nature of the noise present in speech data is not known a priori. In such cases, the performance of speaker identification (SI) or speaker verification (SV) degrades considerably under realistic conditions. Any SR system uses a voice activity detector (VAD) as the front-end subsystem of the whole system. The performance of most VADs deteriorates at the front end of the SR task or system under degraded conditions or in realistic conditions where noise plays a major role. Recently, speech data analysis and processing using Norden E. Huang’s empirical mode decomposition (EMD) combined with Hilbert transform, commonly referred to as Hilbert–Huang transform (HHT), has become an emerging trend. EMD is an a posteriori, adaptive, data analysis tool used in time domain that is widely accepted by the research community. Recently, speech data analysis and speech data processing for speech recognition and SR tasks using EMD have been increasing. EMD-based VAD has become an important adaptive subsystem of the SR system that mostly mitigates the effect of mismatch between the training and the testing phase. Recently, we have developed a VAD algorithm using a zero-frequency filter-assisted peaking resonator (ZFFPR) and EMD. In this article, the efficacy of an EMD-based VAD algorithm is studied at the front end of a text-independent language-independent SI task for the speaker’s data collected in three languages at five different places, such as home, street, laboratory, college campus, and restaurant, under realistic conditions using EDIROL-R09 HR, a 24-bit wav/MP3 recorder. The performance of this proposed SI task is compared against the traditional energy-based VAD in terms of percentage identification rate. In both cases, widely accepted Mel frequency cepstral coefficients are computed by employing frame processing (20-ms frame size and 10-ms frame shift) from the extracted voiced speech regions using the respective VAD techniques from the realistic speech utterances, and are used as a feature vector for speaker modeling using popular Gaussian mixture models. The experimental results showed that the proposed SI task with the VAD algorithm using ZFFPR and EMD at its front end performs better than the SI task with short-term energy-based VAD when used at its front end, and is somewhat encouraging.

Quantitative wearable sensors for objective assessment of Parkinson's disease

There is a rapidly growing interest in the quantitative assessment of Parkinson's disease (PD)-associated signs and disability using wearable technology. Both persons with PD and their clinicians see advantages in such developments. Specifically, quantitative assessments using wearable technology may allow for continuous, unobtrusive, objective, and ecologically valid data collection. Also, this approach may improve patient-doctor interaction, influence therapeutic decisions, and ultimately ameliorate patients' global health status. In addition, such measures have the potential to be used as outcome parameters in clinical trials, allowing for frequent assessments; eg, in the home setting. This review discusses promising wearable technology, addresses which parameters should be prioritized in such assessment strategies, and reports about studies that have already investigated daily life issues in PD using this new technology.

A common optimization principle for motor execution in healthy subjects and parkinsonian patients

Recent research on Parkinson's disease (PD) has emphasized that parkinsonian movement, although bradykinetic, shares many attributes with healthy behavior. This observation led to the suggestion that bradykinesia in PD could be due to a reduction in motor motivation. This hypothesis can be tested in the framework of optimal control theory, which accounts for many characteristics of healthy human movement while providing a link between the motor behavior and a cost/benefit trade-off. This approach offers the opportunity to interpret movement deficits of PD patients in the light of a computational theory of normal motor control. We studied 14 PD patients with bilateral subthalamic nucleus (STN) stimulation and 16 age-matched healthy controls, and tested whether reaching movements were governed by similar rules in these two groups. A single optimal control model accounted for the reaching movements of healthy subjects and PD patients, whatever the condition of STN stimulation (on or off). The choice of movement speed was explained in all subjects by the existence of a preset dynamic range for the motor signals. This range was idiosyncratic and applied to all movements regardless of their amplitude. In PD patients this dynamic range was abnormally narrow and correlated with bradykinesia. STN stimulation reduced bradykinesia and widened this range in all patients, but did not restore it to a normal value. These results, consistent with the motor motivation hypothesis, suggest that constrained optimization of motor effort is the main determinant of movement planning (choice of speed) and movement production, in both healthy and PD subjects.

Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed

While balance and gait limitations are hallmarks of multiple sclerosis (MS), standard stopwatch-timed measures practical for use in the clinic are insensitive in minimally affected patients. This prevents early detection and intervention for mobility problems. The study sought to determine if body-worn sensors could detect differences in balance and gait between people with MS with normal walking speeds and healthy controls. Thirty-one MS and twenty-eight age- and sex-matched control subjects were tested using body-worn sensors both during quiet stance and gait (Timed Up and Go test, TUG). Results were compared to stopwatch-timed measures. Stopwatch durations of the TUG and Timed 25 Foot Walk tests were not significantly different between groups. However, during quiet stance with eyes closed, people with MS had significantly greater sway acceleration amplitude than controls (p=0.02). During gait, people with MS had greater trunk angular range of motion in roll (medio-lateral flexion, p=0.017) and yaw (axial rotation, p=0.026) planes. Turning duration through 180° was also longer in MS (p=0.031). Thus, body-worn motion sensors detected mobility differences between MS and healthy controls when traditional timed tests could not. This portable technology provides objective and quantitative mobility data previously not obtainable in the clinic, and may prove a useful outcome measure for early mobility changes in MS.

Wearable systems with minimal set-up for monitoring and training of balance and mobility

With the objective to release solutions which can be easily manageable by their final users, including older users, we worked to design methods and devices which rely on a minimal set-up for monitoring and rehabilitation of balance and mobility. A single inertial sensing unit, typically worn on the trunk, was hence engineered to accomplish for activity monitoring and event detection (including fall detection), tremor rejection, instrumented clinical tests (e.g. stabilometry, Timed-Up and Go), and sensory biofeedback (audio, visual or tactile). The sensing unit is wirelessly connected with a processing unit, which can in turn act as a gateway to remote applications or caregivers. Promising results were obtained, which may pave the way to novel intensive and pervasive neurorehabilitation strategies.