Next Steps in Wearable Technology and Community Ambulation in Multiple Sclerosis

Examining the validity of smart glasses in measuring spatiotemporal parameters of gait among people with Parkinson's disease

BACKGROUND

Gait impairment is an early marker of Parkinson's disease (PD) and is frequently monitored to evaluate disease progression. Wearable sensors are increasingly being used to quantify gait in the real-world setting among people with PD (pwPD). Particularly, embedding wearables on devices or clothing that are worn daily may represent a useful strategy to improve compliance and regular monitoring of gait.

RESEARCH QUESTION

The current investigation examined the validity of innovative smart glasses to measure gait among pwPD.

METHODS

Participants wore the smart glasses and 6 APDM gait sensors simultaneously, while performing two walking tasks: the 3-meters Timed Up and Go test (TUG) and the 7-meters Stand and Walk (SAW) test. The following spatiotemporal gait parameters were calculated from the data collected using the two different devices: step time, step length, swing percentage, TUG duration, turn duration, and turn velocity.

RESULTS

A total of 31 pwPD (mean age=68.6±8.5 years; 35.48 % female(N=11), mean Unified Parkinson's Disease Rating Scale (UPDRS) total score=32.1±14.7) participated in the study. Smart glasses achieved high validity in measuring step time (ICC=0.92, p=0.01) and TUG duration (ICC=0.96, p=0.03) compared to APDM sensors. On the other hand, the smart glasses did not achieve adequate validity when measuring step length, swing percentage, turn duration or turn velocity.

SIGNIFICANCE

The current study suggests that smart glasses has the potential to measure TUG and step time in individuals living with PD. However, further research is needed to improve algorithms for sensors worn on the head.

Information processing speed and disease severity predict real-world ambulation in persons with multiple sclerosis

BACKGROUND

Impairments in real-world gait quality and quantity are multifaceted for individuals with multiple sclerosis (MS), encompassing mobility, cognition, and fear of falling. However, these factors are often examined independently, limiting insights into the combined contributions they make to real-world ambulation.

RESEARCH QUESTION

How do mobility, cognition, and fear of falling contribute to real-world gait quality and quantity in individuals with MS?

METHODS

Twenty individuals with MS underwent a series of cognitive assessments, including the Paced Auditory Serial Addition Test (PASAT), Symbol Digits Modalities Test (SDMT), Stroop Test, and the Selective Reminding Test (SRT). Participants also completed the Falls Efficacy Scale - International (FES-I) and walking impairment using the Patient Determined Disease Steps (PDDS). Following the in-lab session, participants wore an inertial sensor on their lower back and asked to go about their typical daily routines for three days. Metrics of gait speed, stride regularity, time spent walking, and total bouts were extracted from the real-world data.

RESULTS

Significant correlations were found between both real-world gait speed and stride regularity and the SDMT, FES-I, and PDDS. Backward linear regression analysis was conducted for gait speed and stride regularity, with PDDS and SDMT included in the final model for both metrics. These variables explained 63% of the variance in gait speed and 69% of the variance in stride regularity. Results were not significant for gait quantity after adjusting for age and sex.

SIGNIFICANCE

The study's results provide insight regarding the roles of cognition, walking impairment, and fear of falling on real-world ambulation. Deeper understanding of these contributions can inform the development of targeted interventions that aim to improve walking. Additionally, the absence of significant correlations between gait metrics, cognition, and fear of falling with gait quantity underscores the need for further research to identify factors that increased walking in this population.

Evaluating Cognitive-Motor Interference in Multiple Sclerosis: A Technology-Based Approach

BACKGROUND

People with multiple sclerosis (PwMS) frequently present both cognitive and motor impairments, so it is reasonable to assume they may have difficulties in executing dual-tasks (DT). The aim of the present study is to identify novel technology-based parameters to assess cognitive-motor interference (CMI) in PwMS. In particular, we focused on the definition of dual-task cost (DTC) measures using wearable and portable tools such as insoles and mobile apps.

METHODS

All participants underwent a verbal fluency task (cognitive single-task, ST), a motor ST of walking, and a combination of these tasks (DT). Number of words uttered in the cognitive ST and steps recorded by insoles were used to calculate the motor and cognitive DTC.

RESULTS

The number of steps strongly correlated with the walked meters for both single- (r = 0.88, p < 0.05) and dual- (r = 0.91, p < 0.05) tasks. Motor but not cognitive performances significantly worsened during DT. Over the cognitive ST and DT, the number of pronounced words progressively decreased, probably due to the activation of different cognitive processes. Cognitive efforts could be the cause of cognitive task prioritization.

CONCLUSIONS

Our findings promote the use of low-cost devices to assess CMI easily in the clinical context and to detect ecologically valid DT impairments.

Range of motion abnormalities in the lower limb joints during gait in youth with multiple sclerosis

BACKGROUND

The major objective of this study was to examine whether differences occur in the joint angles of the major lower limb joints while walking in youth with multiple sclerosis (YwMS) compared to age-gender-matched healthy youngsters.

METHODS

Gait analysis was collected using a six-camera Cartesian Optoelectronic Dynamic Anthropometer (CODA) 3D motion analysis system. To determine the gait normality in the YwMS group, we compared our results to reference gait data from normal youngsters. Gait data was divided according to gender and age subgroups (8-14, 15-18 years old).

RESULTS

The total sample included 26 YwMS (12 girls, 14 boys), 11 in the 8-14 age subgroup, and 15 in the 15-18 age subgroup. The mean expanded disability status scale score was 1.3 (S.D=1.0), indicating minimal disability. The total range of hip motion was significantly less in the YwMS group aged 15-18 (both genders) compared with the normative values. Additionally, the maximum flexion in the knee joint during gait was significantly less in boys in the 15 to 18 age subgroup (p<0.001). No differences were found in the hip and knee joint angles in the 8-14 age subgroup.

CONCLUSIONS

YwMS experience modifications in their gait pattern compared with age-gender-matched healthy youngsters. The MS teenagers demonstrated less range of movement in the hip joints and walked slower at a decreased pace than the healthy teenagers. By tracking gait patterns, healthcare providers can identify subtle changes that might facilitate timely interventions to prevent further mobility impairment in this young population.

Wearable devices for gait and posture monitoring via telemedicine in people with movement disorders and multiple sclerosis: a systematic review

INTRODUCTION

Wearable devices and telemedicine are increasingly used to track health-related parameters across patient populations. Since gait and postural control deficits contribute to mobility deficits in persons with movement disorders and multiple sclerosis, we thought it interesting to evaluate devices in telemedicine for gait and posture monitoring in such patients.

METHODS

For this systematic review, we searched the electronic databases MEDLINE (PubMed), SCOPUS, Cochrane Library, and SPORTDiscus. Of the 452 records retrieved, 12 met the inclusion/exclusion criteria. Data about (1) study characteristics and clinical aspects, (2) technical, and (3) telemonitoring and teleconsulting were retrieved, The studies were quality assessed.

RESULTS

All studies involved patients with Parkinson's disease; most used triaxial accelerometers for general assessment (n = 4), assessment of motor fluctuation (n = 3), falls (n = 2), and turning (n = 3). Sensor placement and count varied widely across studies. Nine used lab-validated algorithms for data analysis. Only one discussed synchronous patient feedback and asynchronous teleconsultation.

CONCLUSIONS

Wearable devices enable real-world patient monitoring and suggest biomarkers for symptoms and behaviors related to underlying gait disorders. thus enriching clinical assessment and personalized treatment plans. As digital healthcare evolves, further research is needed to enhance device accuracy, assess user acceptability, and integrate these tools into telemedicine infrastructure.

PROSPERO REGISTRATION

CRD42022355460.

Progression events defined by home-based assessment of motor function in multiple sclerosis: protocol of a prospective study

Background

This study relates to emerging concepts of appropriate trial designs to evaluate effects of intervention on the accumulation of irreversible disability in multiple sclerosis (MS). Major starting points of our study are the known limitations of current definitions of disability progression by rater-based clinical assessment and the high relevance of gait and balance dysfunctions in MS. The study aims to explore a novel definition of disease progression using repeated instrumental assessment of relevant motor functions performed by patients in their home setting.

Methods

The study is a prospective single-center observational cohort study with the primary outcome acquired by participants themselves, a home-based assessment of motor functions based on an RGB-Depth (RGB-D) camera, a camera that provides both depth (D) and color (RGB) data. Participants are instructed to perform and record a set of simple motor tasks twice a day over a one-week period every 6 months. Assessments are complemented by a set of questionnaires. Annual research grade assessments are acquired at dedicated study visits and include clinical ratings as well as structural imaging (MRI and optical coherence tomography). In addition, clinical data from routine visits is provided semiannually by treating neurologists. The observation period is 24 months for the primary endpoint with an additional clinical assessment at 27 month to confirm progression defined by the Expanded Disability Status Scale (EDSS). Secondary analyses aim to explore the time course of changes in motor parameters and performance of the novel definition against different alternative definitions of progression in MS. The study was registered at Deutsches Register für Klinische Studien (DRKS00027042).

Discussion

The study design presented here investigates disease progression defined by marker-less home-based assessment of motor functions against 3-month confirmed disease progression (3 m-CDP) defined by the EDSS. The technical approach was chosen due to previous experience in lab-based settings. The observation time per participant of 24, respectively, 27 months is commonly conceived as the lower limit needed to study disability progression. Defining a valid digital motor outcome for disease progression in MS may help to reduce observation times in clinical trials and add confidence to the detection of progression events in MS.

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.

Assessment of Traumatic Stress Symptoms During the Acute Posttrauma Period

A substantial majority of adults in the United States will experience a potentially traumatic event (PTE) in their lifetime. A considerable proportion of those individuals will go on to develop posttraumatic stress disorder (PTSD). Distinguishing between those who will develop PTSD and those who will recover, however, remains as a challenge to the field. Recent work has pointed to the increased potential of identifying individuals at greatest risk for PTSD through repeated assessment during the acute posttrauma period, the 30-day period after the PTE. Obtaining the necessary data during this period, however, has proven to be a challenge. Technological innovations such as personal mobile devices and wearable passive sensors have given the field new tools to capture nuanced in vivo changes indicative of recovery or nonrecovery. Despite their potential, there are numerous points for clinicians and research teams to consider when implementing these technologies into acute posttrauma care. The limitations of this work and considerations for future research in the use of technology during the acute posttrauma period are discussed.

Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis

The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.

Use of a wearable accelerometer to evaluate physical frailty in people receiving haemodialysis

BACKGROUND

Physical frailty is a major health concern among people receiving haemodialysis (HD) for stage-5 chronic kidney disease (CKD-5). Wearable accelerometers are increasingly being recommended to objectively monitor activity levels in CKD-5 and recent research suggests they may also represent an innovative strategy to evaluate physical frailty in vulnerable populations. However, no study has yet explored whether wearable accelerometers may be utilised to assess frailty in the context of CKD-5-HD. Therefore, we aimed to examine the diagnostic performance of a research-grade wearable accelerometer in evaluating physical frailty in people receiving HD.

METHODS

Fifty-nine people receiving maintenance HD [age = 62.3 years (SD = 14.9), 40.7% female] participated in this cross-sectional study. Participants wore a uniaxial accelerometer (ActivPAL) for seven consecutive days and the following measures were recorded: total number of daily steps and sit-to-stand transitions, number of daily steps walked with cadence < 60 steps/min, 60-79 steps/min, 80-99 steps/min, 100-119 steps/min, and ≥ 120 steps/min. The Fried phenotype was used to evaluate physical frailty. Receiver operating characteristics (ROC) analyses were performed to examine the diagnostic accuracy of the accelerometer-derived measures in detecting physical frailty status.

RESULTS

Participants classified as frail (n = 22, 37.3%) had a lower number of daily steps (2363 ± 1525 vs 3585 ± 1765, p = 0.009), daily sit-to-stand transitions (31.8 ± 10.3 vs 40.6 ± 12.1, p = 0.006), and lower number of steps walked with cadence of 100-119 steps/min (336 ± 486 vs 983 ± 797, p < 0.001) compared to their non-frail counterparts. In ROC analysis, the number of daily steps walked with cadence ≥ 100 steps/min exhibited the highest diagnostic performance (AUC = 0.80, 95% CI: 0.68-0.92, p < 0.001, cut-off ≤ 288 steps, sensitivity = 73%, specificity = 76%, PPV = 0.64, NPV = 0.82, accuracy = 75%) in detecting physical frailty.

CONCLUSIONS

This study provided initial evidence that a wearable accelerometer may be a useful tool in evaluating physical frailty in people receiving HD. While the total number of daily steps and sit-to-stand transitions could significantly discriminate frailty status, the number of daily steps walked with cadences reflecting moderate to vigorous intensity of walking may be more useful in monitoring physical frailty in people receiving HD.

An observational study to assess validity and reliability of smartphone sensor-based gait and balance assessments in multiple sclerosis: Floodlight GaitLab protocol

Background

Gait and balance impairments are often present in people with multiple sclerosis (PwMS) and have a significant impact on quality of life and independence. Gold-standard quantitative tools for assessing gait and balance such as motion capture systems and force plates usually require complex technical setups. Wearable sensors, including those integrated into smartphones, offer a more frequent, convenient, and minimally burdensome assessment of functional disability in a home environment. We developed a novel smartphone sensor-based application (Floodlight) that is being used in multiple research and clinical contexts, but a complete validation of this technology is still lacking.

Methods

This protocol describes an observational study designed to evaluate the analytical and clinical validity of Floodlight gait and balance tests. Approximately 100 PwMS and 35 healthy controls will perform multiple gait and balance tasks in both laboratory-based and real-world environments in order to explore the following properties: (a) concurrent validity of the Floodlight gait and balance tests against gold-standard assessments; (b) reliability of Floodlight digital measures derived under different controlled gait and balance conditions, and different on-body sensor locations; (c) ecological validity of the tests; and (d) construct validity compared with clinician- and patient-reported assessments.

Conclusions

The Floodlight GaitLab study (ISRCTN15993728) represents a critical step in the technical validation of Floodlight technology to measure gait and balance in PwMS, and will also allow the development of new test designs and algorithms.

Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis

Background

Consumer-grade fitness trackers offer exciting opportunities to study persons with chronic diseases in greater detail and in their daily-life environment. However, attempts to bring fitness tracker measurement campaigns from tightly controlled clinical environments to home settings are often challenged by deteriorating study compliance or by organizational and resource limitations.

Objectives

By revisiting the study design and patient-reported experiences of a partly remote study with fitness trackers (BarKA-MS study), we aimed to qualitatively explore the relationship between overall study compliance and scalability. On that account, we aimed to derive lessons learned on strengths, weaknesses, and technical challenges for the conduct of future studies.

Methods

The two-phased BarKA-MS study employed Fitbit Inspire HR and electronic surveys to monitor physical activity in 45 people with multiple sclerosis in a rehabilitation setting and in their natural surroundings at home for up to 8 weeks. We examined and quantified the recruitment and compliance in terms of questionnaire completion and device wear time. Furthermore, we qualitatively evaluated experiences with devices according to participants' survey-collected reports. Finally, we reviewed the BarKA-MS study conduct characteristics for its scalability according to the Intervention Scalability Assessment Tool checklist.

Results

Weekly electronic surveys completion reached 96%. On average, the Fitbit data revealed 99% and 97% valid wear days at the rehabilitation clinic and in the home setting, respectively. Positive experiences with the device were predominant: only 17% of the feedbacks had a negative connotation, mostly pertaining to perceived measurement inaccuracies. Twenty-five major topics and study characteristics relating to compliance were identified. They broadly fell into the three categories: "effectiveness of support measures", "recruitment and compliance barriers", and "technical challenges". The scalability assessment revealed that the highly individualized support measures, which contributed greatly to the high study compliance, may face substantial scalability challenges due to the strong human involvement and limited potential for standardization.

Conclusion

The personal interactions and highly individualized participant support positively influenced study compliance and retention. But the major human involvement in these support actions will pose scalability challenges due to resource limitations. Study conductors should anticipate this potential compliance-scalability trade-off already in the design phase.

Pedometers and Accelerometers in Multiple Sclerosis: Current and New Applications

Pedometers and accelerometers have become commonplace for the assessment of physical behaviors (e.g., physical activity and sedentary behavior) in multiple sclerosis (MS) research. Current common applications include the measurement of steps taken and the classification of physical activity intensity, as well as sedentary behavior, using cut-points methods. The existing knowledge and applications, coupled with technological advances, have spawned new opportunities for using those motion sensors in persons with MS, and these include the utilization of the data as biomarkers of disease severity and progression, perhaps in clinical practice. Herein, we discuss the current state of knowledge on the validity and applications of pedometers and accelerometers in MS, as well as new opportunities and strategies for the improved assessment of physical behaviors and disease progression, and consequently, personalized care.

How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.

Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach

Transcranial direct current stimulation (tDCS) has emerged as an appealing rehabilitative approach to improve brain function, with promising data on gait and balance in people with multiple sclerosis (MS). However, single variable weights have not yet been adequately assessed. Hence, the aim of this pilot randomized controlled trial was to evaluate the tDCS effects on balance and gait in patients with MS through a machine learning approach. In this pilot randomized controlled trial (RCT), we included people with relapsing−remitting MS and an Expanded Disability Status Scale >1 and <5 that were randomly allocated to two groups—a study group, undergoing a 10-session anodal motor cortex tDCS, and a control group, undergoing a sham treatment. Both groups underwent a specific balance and gait rehabilitative program. We assessed as outcome measures the Berg Balance Scale (BBS), Fall Risk Index and timed up-and-go and 6-min-walking tests at baseline (T0), the end of intervention (T1) and 4 (T2) and 6 weeks after the intervention (T3) with an inertial motion unit. At each time point, we performed a multiple factor analysis through a machine learning approach to allow the analysis of the influence of the balance and gait variables, grouping the participants based on the results. Seventeen MS patients (aged 40.6 ± 14.4 years), 9 in the study group and 8 in the sham group, were included. We reported a significant repeated measures difference between groups for distances covered (6MWT (meters), p < 0.03). At T1, we showed a significant increase in distance (m) with a mean difference (MD) of 37.0 [−59.0, 17.0] (p = 0.003), and in BBS with a MD of 2.0 [−4.0, 3.0] (p = 0.03). At T2, these improvements did not seem to be significantly maintained; however, considering the machine learning analysis, the Silhouette Index of 0.34, with a low cluster overlap trend, confirmed the possible short-term effects (T2), even at 6 weeks. Therefore, this pilot RCT showed that tDCS may provide non-sustained improvements in gait and balance in MS patients. In this scenario, machine learning could suggest evidence of prolonged beneficial effects.

Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis

BACKGROUND

One in two people with multiple sclerosis (PwMS) will fall in a three-month period. Predicting which patients will fall remains a challenge for clinicians. Standardized functional assessments provide insight into balance deficits and fall risk but their use has been limited to supervised visits.

RESEARCH QUESTION

The study aim was to characterize unsupervised 30-second chair stand test (30CST) performance using accelerometer-derived metrics and assess its ability to classify fall status in PwMS compared to supervised 30CST.

METHODS

Thirty-seven PwMS (21 fallers) performed instrumented supervised and unsupervised 30CSTs with a single wearable sensor on the thigh. In unsupervised conditions, participants performed bi-hourly 30CSTs and rated their balance confidence and fatigue over 48-hours. ROC analysis was used to classify fall status for 30CST performance.

RESULTS

Non-fallers (p = 0.02) but not fallers (p = 0.23) differed in their average unsupervised 30CST performance (repetitions) compared to their supervised performance. The unsupervised maximum number of 30CST repetitions performed optimized ROC classification AUC (0.79), accuracy (78.4%) and specificity (90.0%) for fall status with an optimal cutoff of 17 repetitions.

SIGNIFICANCE

Brief durations of instrumented unsupervised monitoring as an adjunct to routine clinical assessments could improve the ability for predicting fall risk and fluctuations in functional mobility in PwMS.

Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development and Usability Study

Background

Instrumented assessment of motor symptoms has emerged as a promising extension to the clinical assessment of several movement disorders. The use of mobile and inexpensive technologies such as some markerless motion capture technologies is especially promising for large-scale application but has not transitioned into clinical routine to date. A crucial step on this path is to implement standardized, clinically applicable tools that identify and control for quality concerns.

Objective

The main goal of this study comprises the development of a systematic quality control (QC) procedure for data collected with markerless motion capture technology and its experimental implementation to identify specific quality concerns and thereby rate the usability of recordings.

Methods

We developed a post hoc QC pipeline that was evaluated using a large set of short motor task recordings of healthy controls (2010 recordings from 162 subjects) and people with multiple sclerosis (2682 recordings from 187 subjects). For each of these recordings, 2 raters independently applied the pipeline. They provided overall usability decisions and identified technical and performance-related quality concerns, which yielded respective proportions of their occurrence as a main result.

Results

The approach developed here has proven user-friendly and applicable on a large scale. Raters’ decisions on recording usability were concordant in 71.5%-92.3% of cases, depending on the motor task. Furthermore, 39.6%-85.1% of recordings were concordantly rated as being of satisfactory quality whereas in 5.0%-26.3%, both raters agreed to discard the recording.

Conclusions

We present a QC pipeline that seems feasible and useful for instant quality screening in the clinical setting. Results confirm the need of QC despite using standard test setups, testing protocols, and operator training for the employed system and by extension, for other task-based motor assessment technologies. Results of the QC process can be used to clean existing data sets, optimize quality assurance measures, as well as foster the development of automated QC approaches and therefore improve the overall reliability of kinematic data sets.

Split-Belt Training but Not Cerebellar Anodal tDCS Improves Stability Control and Reduces Risk of Fall in Patients with Multiple Sclerosis

The objective of this study was to examine the therapeutic potential of multiple sessions of training on a split-belt treadmill (SBT) combined with cerebellar anodal transcranial direct current stimulation (tDCS) on gait and balance in People with Multiple Sclerosis (PwMS). Twenty-two PwMS received six sessions of anodal (PwMS_real, n = 12) or sham (PwMS_sham, n = 10) tDCS to the cerebellum prior to performing the locomotor adaptation task on the SBT. To evaluate the effect of the intervention, functional gait assessment (FGA) scores and distance walked in 2 min (2MWT) were measured at the baseline (T0), day 6 (T5), and at the 4-week follow up (T6). Locomotor performance and changes of motor outcomes were similar in PwMS_real and PwMS_sham independently from tDCS mode applied to the cerebellum (anodal vs. sham, on FGA, p = 0.23; and 2MWT, p = 0.49). When the data were pooled across the groups to investigate the effects of multiple sessions of SBT training alone, significant improvement of gait and balance was found on T5 and T6, respectively, relative to baseline (FGA, p < 0.001 for both time points). The FGA change at T6 was significantly higher than at T5 (p = 0.01) underlining a long-lasting improvement. An improvement of the distance walked during the 2MWT was also observed on T5 and T6 relative to T0 (p = 0.002). Multiple sessions of SBT training resulted in a lasting improvement of gait stability and endurance, thus potentially reducing the risk of fall as measured by FGA and 2MWT. Application of cerebellar tDCS during SBT walking had no additional effect on locomotor outcomes.

Efficacy of transcranial direct current stimulation in people with multiple sclerosis: a review

BACKGROUND AND PURPOSE

Multiple sclerosis (MS) is a chronic inflammatory disease causing a wide range of symptoms including motor and cognitive impairment, fatigue and pain. Over the last two decades, non-invasive brain stimulation, especially transcranial direct current stimulation (tDCS), has increasingly been used to modulate brain function in various physiological and pathological conditions. However, its experimental applications for people with MS were noted only as recently as 2010 and have been growing since then. The efficacy for use in people with MS remains questionable with the results of existing studies being largely conflicting. Hence, the aim of this review is to paint a picture of the current state of tDCS in MS research grounded on studies applying tDCS that have been done to date.

METHODS

A keyword search was performed to retrieve articles from the earliest article identified until 14 February 2021 using a combination of the groups (1) 'multiple sclerosis', 'MS' and 'encephalomyelitis' and (2) 'tDCS' and 'transcranial direct current stimulation'.

RESULTS

The analysis of the 30 articles included in this review underlined inconsistent effects of tDCS on the motor symptoms of MS based on small sample sizes. However, tDCS showed promising benefits in ameliorating fatigue, pain and cognitive symptoms.

CONCLUSION

Transcranial direct current stimulation is attractive as a non-drug approach in ameliorating MS symptoms, where other treatment options remain limited. The development of protocols tailored to the individual's own neuroanatomy using high definition tDCS and the introduction of network mapping in the experimental designs might help to overcome the variability between studies.

Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities

The increasingly ageing population and the tendency to live alone have led science and engineering researchers to search for health care solutions. In the COVID 19 pandemic, the elderly have been seriously affected in addition to suffering from isolation and its associated and psychological consequences. This paper provides an overview of the RobWell (Robotic-based Well-Being Monitoring and Coaching System for the Elderly in their Daily Activities) system. It is a system focused on the field of artificial intelligence for mood prediction and coaching. This paper presents a general overview of the initially proposed system as well as the preliminary results related to the home automation subsystem, autonomous robot navigation and mood estimation through machine learning prior to the final system integration, which will be discussed in future works. The main goal is to improve their mental well-being during their daily household activities. The system is composed of ambient intelligence with intelligent sensors, actuators and a robotic platform that interacts with the user. A test smart home system was set up in which the sensors, actuators and robotic platform were integrated and tested. For artificial intelligence applied to mood prediction, we used machine learning to classify several physiological signals into different moods. In robotics, it was concluded that the ROS autonomous navigation stack and its autodocking algorithm were not reliable enough for this task, while the robot’s autonomy was sufficient. Semantic navigation, artificial intelligence and computer vision alternatives are being sought.

A Preliminary Investigation of the Effects of Obstacle Negotiation and Turning on Gait Variability in Adults with Multiple Sclerosis

Many falls in persons with multiple sclerosis (PwMS) occur during daily activities such as negotiating obstacles or changing direction. While increased gait variability is a robust biomarker of fall risk in PwMS, gait variability in more ecologically related tasks is unclear. Here, the effects of turning and negotiating an obstacle on gait variability in PwMS were investigated. PwMS and matched healthy controls were instrumented with inertial measurement units on the feet, lumbar, and torso. Subjects completed a walk and turn (WT) with and without an obstacle crossing (OW). Each task was partitioned into pre-turn, post-turn, pre-obstacle, and post-obstacle phases for analysis. Spatial and temporal gait measures and measures of trunk rotation were captured for each phase of each task. In the WT condition, PwMS demonstrated significantly more variability in lumbar and trunk yaw range of motion and rate, lateral foot deviation, cadence, and step time after turning than before. In the OW condition, PwMS demonstrated significantly more variability in both spatial and temporal gait parameters in obstacle approach after turning compared to before turning. No significant differences in gait variability were observed after negotiating an obstacle, regardless of turning or not. Results suggest that the context of gait variability measurement is important. The increased number of variables impacted from turning and the influence of turning on obstacle negotiation suggest that varying tasks must be considered together rather than in isolation to obtain an informed understanding of gait variability that more closely resembles everyday walking.

Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis

Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments could help in prescribing preventative interventions. To this end, retrospective fall status classification commonly serves as an intermediate step in developing prospective fall risk assessments. Previous research has identified measures of gait biomechanics that differ between PwMS who have fallen and those who have not, but these biomechanical indices have not yet been leveraged to detect PwMS who have fallen. Moreover, they require the use of laboratory-based measurement technologies, which prevent clinical deployment. Here we demonstrate that a bidirectional long short-term (BiLSTM) memory deep neural network was able to identify PwMS who have recently fallen with good performance (AUC of 0.88) based on accelerometer data recorded from two wearable sensors during a one-minute walking task. These results provide substantial improvements over machine learning models trained on spatiotemporal gait parameters (21% improvement in AUC), statistical features from the wearable sensor data (16%), and patient-reported (19%) and neurologist-administered (24%) measures in this sample. The success and simplicity (two wearable sensors, only one-minute of walking) of this approach indicates the promise of inexpensive wearable sensors for capturing fall risk in PwMS.

Unobtrusive Sensors for the Assessment of Older Adult's Frailty: A Scoping Review

Ubiquity (devices becoming part of the context) and transparency (devices not interfering with daily activities) are very significant in healthcare monitoring applications for elders. The present study undertakes a scoping review to map the literature on sensor-based unobtrusive monitoring of older adults’ frailty. We aim to determine what types of devices comply with unobtrusiveness requirements, which frailty markers have been unobtrusively assessed, which unsupervised devices have been tested, the relationships between sensor outcomes and frailty markers, and which devices can assess multiple markers. SCOPUS, PUBMED, and Web of Science were used to identify papers published 2010–2020. We selected 67 documents involving non-hospitalized older adults (65+ y.o.) and assessing frailty level or some specific frailty-marker with some sensor. Among the nine types of body worn sensors, only inertial measurement units (IMUs) on the waist and wrist-worn sensors comply with ubiquity. The former can transparently assess all variables but weight loss. Wrist-worn devices have not been tested in unsupervised conditions. Unsupervised presence detectors can predict frailty, slowness, performance, and physical activity. Waist IMUs and presence detectors are the most promising candidates for unobtrusive and unsupervised monitoring of frailty. Further research is necessary to give specific predictions of frailty level with unsupervised waist IMUs.

Smartphone applications to assess gait and postural control in people with multiple sclerosis: A systematic review

BACKGROUND

Methods to effectively assess gait and balance impairments are necessary to guide interventions among people with Multiple Sclerosis (PwMS). Smartphone-based evaluations are becoming popular due to the ubiquitous use of smartphones in society.

OBJECTIVE

To determine the current state of smartphone applications that assess gait and balance among PwMS.

METHODS

Two independent reviewers screened articles retrieved from PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Articles meeting eligibility criteria were summarized and qualitatively discussed. Participant characteristics, validity, reliability, sensitivity and specificity measures, and main results of smartphone-based gait and balance evaluations were summarized. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cross-sectional studies.

RESULTS

Eight articles were included in this review. The studies present mostly with low risk of bias. All studies successfully tested the use of smartphone applications in assessing gait and balance among PwMS. In total, 75% of the studies evaluated the validity; 38% evaluated the reliability, sensitivity, and specificity of smartphone applications to assess gait and balance. Of those, all studies except one found smartphone applications to be appropriately valid, reliable, sensitive, and specific in assessing gait and balance. Most studies (88%) reported PwMS and clinicians as their intended users.

CONCLUSION

There is evidence supporting the use of smartphone applications to assess gait and balance among PwMS. Future studies should further examine the psychometric properties of smartphone-based gait and postural control assessments as well as the sensitivity and specificity to improve the interpretation of the results.

What the Tech? The Management of Neurological Dysfunction Through the Use of Digital Technology

Worldwide, it is estimated that millions of individuals suffer from a neurological disorder which can be the result of head injuries, ischaemic events such as a stroke, or neurodegenerative disorders such as Parkinson's disease (PD) and multiple sclerosis (MS). Problems with mobility and hemiparesis are common for these patients, making daily life, social factors and independence heavily affected. Current therapies aimed at improving such conditions are often tedious in nature, with patients often losing vital motivation and positive outlook towards their rehabilitation. The interest in the use of digital technology in neuro-rehabilitation has skyrocketed in the past decade. To gain insight, a systematic review of the literature in the field was conducting following the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines for three categories: stroke, Parkinson's disease and multiple sclerosis. It was found that the majority of the literature (84%) was in favour of the use of digital technologies in the management of neurological dysfunction; with some papers taking a "neutral" or "against" standpoint. It was found that the use of technologies such as virtual reality (VR), robotics, wearable sensors and telehealth was highly accepted by patients, helped to improve function, reduced anxiety and make therapy more accessible to patients living in more remote areas. The most successful therapies were those that used a combination of conventional therapies and new digital technologies.

The Dresden Protocol for Multidimensional Walking Assessment (DMWA) in Clinical Practice

Walking impairments represent one of the most debilitating symptom areas for people with multiple sclerosis (MS). It is important to detect even slightest walking impairments in order to start and optimize necessary interventions in time to counteract further progression of the disability. For this reason, a regular monitoring through gait analysis is highly necessary. At advanced stages of MS with significant walking impairment, this assessment is also necessary to optimize symptomatic treatment, choose the most suitable walking aid and plan individualized rehabilitation. In clinical practice, walking impairment is only assessed at higher levels of the disease using e.g., the Expanded Disability Status Scale (EDSS). In contrast to the EDSS, standardized functional tests such as walking speed, walking endurance and balance as well as walking quality and gait-related patient-reported outcomes allow a more holistic and sensitive assessment of walking impairment. In recent years, the MS Center Dresden has established a standardized monitoring procedure for the routine multidimensional assessment of gait and balance disorders. In the following protocol, we present the techniques and procedures for the analysis of gait and balance of people with MS at the MS Center Dresden. Patients are assessed with a multidimensional gait analysis at least once a year. This enables long-term monitoring of walking impairment, which allows early active intervention regarding further progression of disease and improves the current standard clinical practice.

Modeling Muscle Synergies as a Gaussian Process: Estimating Unmeasured Muscle Excitations using a Measured Subset

Continuous observation of muscle activity could provide a comprehensive picture of the loads experienced by muscles and joints during daily life. However, a major limitation to the practical application of this approach is the need to have surface electromyography (sEMG) sensors on all involved muscles. In this work, we model the synergistic relationship between muscles as a Gaussian process enabling the inference of unmeasured muscle excitations using a subset of measured data. Specifically, we developed a model for a single subject which uses sEMG data from four leg muscles to estimate the muscle excitation time-series of six other leg muscles during level walking at a self-selected speed. The proposed technique was able to accurately estimate the held-out muscle excitation time-series of the six muscles with correlation coefficients ranging from 0.74 to 0.87 and with mean absolute error less than 3%.

A Gaussian Process Model of Muscle Synergy Functions for Estimating Unmeasured Muscle Excitations Using a Measured Subset

Estimation of muscle excitations from a reduced sensor array could greatly improve current techniques in remote patient monitoring. Such an approach could allow continuous monitoring of clinically relevant biomechanical variables that are ideal for personalizing rehabilitation. In this paper, we introduce the notion of a muscle synergy function which describes the synergistic relationship between a subset of muscles. We develop from first principles an approximation to their behavior using Gaussian process regression and demonstrate the utility of the technique for estimating the excitation time-series of leg muscles during normal walking for nine healthy subjects. Specifically, excitations for six muscles were estimated using surface electromyography (sEMG) data during a finite time interval (called the input window) from four different muscles (called the input muscles) with mean absolute error (MAE) less than 5.0% of the maximum voluntary contraction (MVC) and that accounts for 82-88% of the variance (VAF) in the true excitations. Further, these estimated excitations informed muscle activations with less than 4.0% MAE and 89-93% VAF. We also present a detailed analysis of a number of different modeling choices, including every possible combination of four-, three- and two-muscle input sets, the size and structure of the input window, and the stationarity of the Gaussian process covariance functions. Further, application specific modifications for future use are discussed. The proposed technique lays a foundation to explore the use of reduced wearable sensor arrays and muscle synergy functions for monitoring clinically relevant biomechanics during daily life.

The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review

This article presents an overview of fifty-eight articles dedicated to the evaluation of physical activity in free-living conditions using wearable motion sensors. This review provides a comprehensive summary of the technical aspects linked to sensors (types, number, body positions, and technical characteristics) as well as a deep discussion on the protocols implemented in free-living conditions (environment, duration, instructions, activities, and annotation). Finally, it presents a description and a comparison of the main algorithms and processing tools used for assessing physical activity from raw signals.

Short Bouts of Gait Data and Body-Worn Inertial Sensors Can Provide Reliable Measures of Spatiotemporal Gait Parameters from Bilateral Gait Data for Persons with Multiple Sclerosis

Wearable devices equipped with inertial sensors enable objective gait assessment for persons with multiple sclerosis (MS), with potential use in ambulatory care or home and community-based assessments. However, gait data collected in non-controlled settings are often fragmented and may not provide enough information for reliable measures. This paper evaluates a novel approach to (1) determine the effects of the length of the walking task on the reliability of calculated measures and (2) identify digital biomarkers for gait assessments from fragmented data. Thirty-seven participants (37) diagnosed with relapsing-remitting MS (EDSS range 0 to 4.5) executed two trials, walking 20 m each, with inertial sensors attached to their right and left shanks. Gait events were identified from the medio-lateral angular velocity, and short bouts of gait data were extracted from each trial, with lengths varying from 3 to 9 gait cycles. Intraclass correlation coefficients (ICCs) evaluate the degree of agreement between the two trials of each participant, according to the number of gait cycles included in the analysis. Results show that short bouts of gait data, including at least six gait cycles of bilateral data, can provide reliable gait measurements for persons with MS, opening new perspectives for gait assessment using fragmented data (e.g., wearable devices, community assessments). Stride time variability and asymmetry, as well as stride velocity variability and asymmetry, should be further explored as digital biomarkers to support the monitoring of symptoms of persons with neurological diseases.

Research on lower extremity health in patients with multiple sclerosis: a systematic scoping review

Background

Multiple sclerosis (MS) often affects ambulation and the function of the lower limbs. However, little is known about how much research has been conducted on lower extremity health in patients with MS.

Objective

To analyse empirical studies and their evidence on lower extremity health in patients with MS, in order to identify the need for future studies in key areas.

Methods

A systematic scoping review was conducted. A literature search of Medline (PubMed), CINAHL (EBSCO) and the Cochrane Library databases was performed. The search covered the period up to 15 January 2020 from the earliest records available. This led to the inclusion of 42 empirical articles. The data were analysed using content analysis and quantification techniques.

Results

The research on lower extremity health focused primarily on two main areas: gait and lower extremity muscle strength. Lower extremity health was assessed using a variety of methods, most of which consisted of objective physical tests and gait analysis. Patients with MS had many problems with the health of their lower extremities, which manifested in walking difficulties, balance problems, muscle weaknesses and spasticity. In the feet, pes cavus, claw toes, oedema and altered foot sensation were common.

Conclusions

MS affects lower limb and foot health, and these problems can affect patients’ daily lives. However, the extent of these problems is poorly understood, partly due to the dearth of research on lower limb and foot health. Therefore, further research is warranted in order to better understand the impact of MS on foot and lower limb health in everyday life.

Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis

BACKGROUND

Approximately half of the 2.3 million people with multiple sclerosis (PwMS) will fall in any three-month period. Currently clinicians rely on self-report measures or simple functional assessments, administered at discrete time points, to assess fall risk. Wearable inertial sensors are a promising technology for increasing the sensitivity of clinical assessments to accurately predict fall risk, but current accelerometer-based approaches are limited.

RESEARCH QUESTION

Will metrics derived from wearable accelerometers during a 30-second chair stand test (30CST) correlate with clinical measures of disease severity, balance confidence and fatigue in PwMS, and can these metrics be used to accurately discriminate fallers from non-fallers?

METHODS

Thirty-eight PwMS (21 fallers) completed self-report outcome measures then performed the 30CST while triaxial acceleration data were collected from inertial sensors adhered to the thigh and chest. Accelerometer metrics were derived for the sit-to-stand and stand-to-sit transitions and relationships with clinical metrics were assessed. Finally, the metrics were used to develop a logistic regression model to classify fall status.

RESULTS

Accelerometer-derived metrics were significantly associated with multiple clinical metrics that capture disease severity, balance confidence and fatigue. Performance of a logistic regression for classifying fall status was enhanced by including accelerometer features (accuracy 74%, AUC 0.78) compared to the standard of care (accuracy 68%, AUC 0.74) or patient reported outcomes (accuracy 71%, AUC 0.75).

SIGNIFICANCE

Accelerometer derived metrics were associated with clinically relevant measures of disease severity, fatigue and balance confidence during a balance challenging task. Inertial sensors could feasibly be utilized to enhance the accuracy of functional assessments to identify fall risk in PwMS. Simplicity of these accelerometer-based metrics could facilitate deployment for community-based monitoring.

Gait event detection using a thigh-worn accelerometer

BACKGROUND

Gait event detection is critical for remote gait analysis. Algorithms using a thigh-worn accelerometer for estimating spatiotemporal gait variables have demonstrated clinical utility in monitoring the gait of patients with gait and balance impairment. However, one may obtain accurate estimates of spatiotemporal variables, but with biased estimates of foot contact and foot off events. Some biomechanical analyses depend on accurate gait phase segmentation, but previous studies using a thigh-worn accelerometer have not quantified the error in estimating foot contact and foot off events.

METHODS

Gait events and spatiotemporal gait variables were estimated using a thigh-worn accelerometer from 32 healthy subjects across a range of walking speeds (0.56-1.78 m/s). Ground truth estimates were obtained using vertical ground reaction forces measured using a pressure treadmill. Estimation performance was quantified using absolute error, root mean square error, and correlation analysis.

RESULTS

Across all strides (N = 3,898), the absolute error in estimating foot contact, foot off, stride time, stance time, and swing time was similar to other accelerometer-based techniques (39 ± 28 ms, 28 ± 28 ms, 11 ± 14 ms, 46 ± 31 ms, and 45 ± 30 ms, respectively). The correlation between reference measurements and estimates of bout-average stride time, stance time, and swing time were 1.00, 0.92, and 0.80, respectively. The (5th, 95th) percentiles of the foot contact and foot off estimation errors were (-91 ms, 51 ms) and (-70 ms, 60 ms), the largest of which amounts to about three samples using the 31.25 Hz sampling frequency used in this study.

SIGNIFICANCE

Use of the proposed algorithm for estimating spatiotemporal gait variables is supported by the strong correlations with reference measurements. The gait event estimation error distributions provide bounds on the estimated gait events for enforcing gait phase-dependent task constraints for biomechanical analysis.

Wearable sensors can reliably quantify gait alterations associated with disability in people with progressive multiple sclerosis in a clinical setting

Gait disability in people with progressive multiple sclerosis (MS) is difficult to quantify using existing clinical tools. This study aims to identify reliable and objective gait-based biomarkers to monitor progressive multiple sclerosis (MS) in clinical settings. During routine clinical visits, 57 people with secondary progressive MS and 24 healthy controls walked for 6 minutes wearing three inertial motion sensors. Fifteen gait measures were computed from the sensor data and tested for between-session reliability, for differences between controls and people with moderate and severe MS disability, and for correlation with Expanded Disability Status Scale (EDSS) scores. The majority of gait measures showed good to excellent between-session reliability when assessed in a subgroup of 23 healthy controls and 25 people with MS. These measures showed that people with MS walked with significantly longer step and stride durations, reduced step and stride regularity, and experienced difficulties in controlling and maintaining a stable walk when compared to controls. These abnormalities significantly increased in people with a higher level of disability and correlated with their EDSS scores. Reliable and objective gait-based biomarkers using wearable sensors have been identified. These biomarkers may allow clinicians to quantify clinically relevant alterations in gait in people with progressive MS within the context of regular clinical visits.

A wearable sensor identifies alterations in community ambulation in multiple sclerosis: contributors to real-world gait quality and physical activity

People with multiple sclerosis (pwMS) often suffer from gait impairments. These changes in gait have been well studied in laboratory and clinical settings. A thorough investigation of gait alterations during community ambulation and their contributing factors, however, is lacking. The aim of the present study was to evaluate community ambulation and physical activity in pwMS and healthy controls and to compare in-lab gait to community ambulation. To this end, 104 subjects were studied: 44 pwMS and 60 healthy controls (whose age was similar to the controls). The subjects wore a tri-axial, lower back accelerometer during usual-walking and dual-task walking in the lab and during community ambulation (1 week) to evaluate the amount, type, and quality of activity. The results showed that during community ambulation, pwMS took fewer steps and walked more slowly, with greater asymmetry, and larger stride-to-stride variability, compared to the healthy controls (p < 0.001). Gait speed during most of community ambulation was significantly lower than the in-lab usual-walking value and similar to the in-lab dual-tasking value. Significant group (pwMS /controls)-by-walking condition (in-lab/community ambulation) interactions were observed (e.g., gait speed). Greater disability was associated with fewer steps and reduced gait speed during community ambulation. In contrast, physical fatigue was correlated with sedentary activity, but was not related to any of the measures of community ambulation gait quality including gait speed. This disparity suggests that more than one mechanism contributes to community ambulation and physical activity in pwMS. Together, these findings demonstrate that during community ambulation, pwMS have marked gait alterations in multiple gait features, reminiscent of dual-task walking measured in the laboratory. Disease-related factors associated with these changes might be targets of rehabilitation.

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.