Identifying Gait Deficits in Stroke Patients Using Inertial Sensors

Falls remain a significant problem for stroke patients. Tripping, the main cause of falls, occurs when there is insufficient clearance between the foot and ground. Based on an individual’s gait deficits, different joint angles and coordination patterns are necessary to achieve adequate foot clearance during walking. However, gait deficits are typically only quantified in a research or clinical setting, and it would be helpful to use wearable devices – such as accelerometers – to quantify gait disorders in real-world situations. Therefore, the objective of this project was to understand gait characteristics that influence the risk of tripping, and to detect these characteristics using accelerometers. Thirty-five participants with a range of walking abilities performed normal walking and attempted to avoid tripping on an unexpected object while gait characteristics were quantified using motion capture techniques and accelerometers. Multiple regression was used to identify the relationship between joint coordination and foot clearance, and multiple analysis of variance was used to determine characteristics of gait that differ between demographic groups, as well as those that enable obstacle avoidance. Machine learning techniques were employed to detect joint angles and the risk of tripping from patterns in accelerometer signals. Measures of foot clearance that represent toe height throughout swing instead of at a single time point are more sensitive to changes in joint coordination, with hip-knee coordination during midswing having the greatest effect. Participants with a history of falls or stroke perform worse than older non-fallers and young adults on many factors related to falls risk, however, there are no differences in the ability to avoid an unexpected obstacle between these groups. Individuals with an inability to avoid an obstacle have lower scores on functional evaluations, exhibit limited sagittal plane joint range of motion during swing, and adopt a conservative walking strategy. Machine learning processes can be used to predict knee range of motion and classify individuals at risk for tripping based on an ankle-worn accelerometer. This work is significant because a portable device that detects gait characteristics relevant to the risk of tripping without expensive motion capture technology may reduce the risk of falls for stroke patients

Sit-to-Stand Phases Detection by Inertial Sensors

The Sit-to-Stand(STS) is defined as the transition from the sitting to standing position. It is commonly adopted in clinical practice because musculoskeletal or neurological degenerative disorders, as well as the natural process of ageing, deter-mine an increased difficulty in rising up from a seated position. This study aimed to detect the Sit To Stand phases using data from inertial sensors. Due to the high variability of this movement, and, consequently the difficulty to define events by thresholds, we used the machine learning. We collected data from 27 participants (13 females,24.37\ub13.32 years old). They wore 10 Inertial Sensors placed on: trunk,back(L4-L5),left and right thigh, tibia, and ankles. The par-ticipants were asked to stand from an height adjustable chair for 10 times. The STS exercises were recorded separately. The starting and ending points of each phase were identified by key events. The pre-processing included phases splitting in epochs. The features extracted were: mean, standard deviation, RMS, Max and min, COV and first derivative. The features were on the epochs for each sensor. To identify the most fitting classifier, two classifier algorithms,K-nearest Neighbours( KNN) and Support Vector Machine (SVM) were trained. From the data recorded, four dataset were created varying the epochs duration, the number of sensors. The validation model used to train the classifier. As validation model, we compared the results of classifiers trained using Kfold and Leave One Subject out (LOSO) models. The classifier performances were evaluated by confusion matrices and the F1 scores. The classifiers trained using LOSO technique as validation model showed higher values of predictive accuracy than the ones trained using Kfold. The predictive accuracy of KNN and SVM were reported below: \u2022 KFold \u2013 mean of overall predictive accuracy KNN: 0.75; F1 score: REST 0.86, TRUNK LEANING 0.35,STANDING 0.60,BALANCE 0.54, SITTING 0.55 \u2013 mean of overall predictive accuracy SVM: 0.75; F1 score: REST 0.89, TRUNK LEANING 0.48,STANDING 0.48,BALANCE 0.59, SITTING 0.62 \u2022 LOSO \u2013 mean of overall predictive accuracy KNN: 0.93; F1 score: REST 0.96, TRUNK LEANING 0.79,STANDING 0.89,BALANCE 0.95, SITTING 0.88 \u2013 mean of overall predictive accuracy SVM: 0.95; F1 score phases: REST 0.98, TRUNK LEANING 0.86,STANDING 0.91,BALANCE 0.98, SIT-TING 0.9

The use of wearable inertial measurement units to assess gait and balance outcomes related to fall risk among older adults

Due to the prevalence and associated health, social and economic costs of falls among older adults, this thesis originally aimed to identify a more robust and objective way of assessing fall risk factors with the use of wearable inertial measurement units (IMU). However, due to unforeseen circumstances, the direction of the thesis had to be changed. Therefore, the thesis aimed to investigate whether gait and balance outcomes related to fall risk, when measured with wearable IMUs are sensitive to conditions which may replicate clinical and habitual environments. In Study one, a systematic scoping review was conducted to identify characteristic differences between fallers and non-fallers with the use of IMUs. The lower trunk was the most common anatomical location, whilst walking a predetermined distance indoors was the most common test used with IMUs to distinguish between fallers and non-fallers. In Study two, seventeen older and seventeen younger adults performed multiple walking and standing tasks in a laboratory. Older adults had a lower root mean square of the IMU acceleration signal, harmonic ratio and greater step time asymmetry compared to younger adults. The use of a cognitive dual task caused gait to be slower and less symmetrical among older and younger adults. Trunk displacement to quantify trunk sway during quiet standing was greater among older adults and increased as standing conditions became more difficult. Older adults exhibited distinct differences in gait when walking indoors and outdoors. The results of Study two suggested that IMUs may identify differences between older and younger adults regarding walking speed and time to completion of clinical tests, even when a stopwatch could not. In Study three, twenty older and twenty younger adults had IMUs attached to different anatomical locations during waking hours. There were differences in all gait variables when walking supervised in the laboratory and unsupervised in habitual indoor environments for both older and younger adults. There were also large differences in gait variables when walking indoors and outdoors. These results suggest the need for future studies in continuous, outdoor and unsupervised free-living conditions, with regards to fall risk assessments. This thesis demonstrates that gait and balance outcomes related to fall risk, when measured using wearable IMUs, are sensitive to conditions resembling habitual and clinical environments among both older and younger adults. This could prove valuable for the enhancement of future fall risk research

Machine Learning-based Detection of Compensatory Balance Responses and Environmental Fall Risks Using Wearable Sensors

Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide, with serious and costly consequences. Compensatory balance responses (CBRs) are reactions to recover stability following a loss of balance, potentially resulting in a fall if sufficient recovery mechanisms are not activated. While performance of CBRs are demonstrated risk factors for falls in seniors, the frequency, type, and underlying cause of these incidents occurring in everyday life have not been well investigated. This study was spawned from the lack of research on development of fall risk assessment methods that can be used for continuous and long-term mobility monitoring of the geri- atric population, during activities of daily living, and in their dwellings. Wearable sensor systems (WSS) offer a promising approach for continuous real-time detection of gait and balance behavior to assess the risk of falling during activities of daily living. To detect CBRs, we record movement signals (e.g. acceleration) and activity patterns of four muscles involving in maintaining balance using wearable inertial measurement units (IMUs) and surface electromyography (sEMG) sensors. To develop more robust detection methods, we investigate machine learning approaches (e.g., support vector machines, neural networks) and successfully detect lateral CBRs, during normal gait with accuracies of 92.4% and 98.1% using sEMG and IMU signals, respectively. Moreover, to detect environmental fall-related hazards that are associated with CBRs, and affect balance control behavior of seniors, we employ an egocentric mobile vision system mounted on participants chest. Two algorithms (e.g. Gabor Barcodes and Convolutional Neural Networks) are developed. Our vision-based method detects 17 different classes of environmental risk factors (e.g., stairs, ramps, curbs) with 88.5% accuracy. To the best of the authors knowledge, this study is the first to develop and evaluate an automated vision-based method for fall hazard detection

Development of a real-time classifier for the identification of the Sit-To-Stand motion pattern

The Sit-to-Stand (STS) movement has significant importance in clinical practice, since it is an indicator of lower limb functionality. As an optimal trade-off between costs and accuracy, accelerometers have recently been used to synchronously recognise the STS transition in various Human Activity Recognition-based tasks. However, beyond the mere identification of the entire action, a major challenge remains the recognition of clinically relevant phases inside the STS motion pattern, due to the intrinsic variability of the movement. This work presents the development process of a deep-learning model aimed at recognising specific clinical valid phases in the STS, relying on a pool of 39 young and healthy participants performing the task under self-paced (SP) and controlled speed (CT). The movements were registered using a total of 6 inertial sensors, and the accelerometric data was labelised into four sequential STS phases according to the Ground Reaction Force profiles acquired through a force plate. The optimised architecture combined convolutional and recurrent neural networks into a hybrid approach and was able to correctly identify the four STS phases, both under SP and CT movements, relying on the single sensor placed on the chest. The overall accuracy estimate (median [95% confidence intervals]) for the hybrid architecture was 96.09 [95.37 - 96.56] in SP trials and 95.74 [95.39 \u2013 96.21] in CT trials. Moreover, the prediction delays ( 4533 ms) were compatible with the temporal characteristics of the dataset, sampled at 10 Hz (100 ms). These results support the implementation of the proposed model in the development of digital rehabilitation solutions able to synchronously recognise the STS movement pattern, with the aim of effectively evaluate and correct its execution

Evaluation of Wearable Sensors as an Older Adult Fall Risk Assessment Tool

Falls are common in the geriatric population, with approximately one third of older adults falling each year. Falls can result in lasting physical and psychological consequences and cost approximately $20 billion per year in the United States. Wearable sensors can be used for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical care and older adult living environments. The objectives of this study were to evaluate eyes open and eyes closed static posturography in older adults; provide in-depth analysis of the differences between single-task and dual-task gait in elderly individuals and the relation to faller status; generate models for wearable-sensor-based fall risk classification in older adults and identify the optimal sensor type, location, combination, and modelling method for walking with and without a cognitive load task; and compare wearable-sensor-based fall risk classification performance to clinical assessment-based and posturography-based fall risk classification outcomes. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence; 47 non-fallers, 28 fallers based on 6 month prospective fall occurrence with retrospective fallers excluded) walked 7.62 m under single-task (ST) and dual-task (DT) conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, static posturography with eyes open and closed, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. Feature selection was performed using Relief-F, Fast Correlation-Based Filter (FCBF), and Correlation based Feature Selection (CFS). For static posturography, measures sensitive to anterior-posterior motion and medial-lateral centre of pressure (CoP) velocity were greater under eyes closed compared to eyes open conditions for prospective non-fallers, fallers, and multi-fallers. For prospective multi-fallers, medial-lateral range and root-mean square distance from the mean were also greater when visual input was removed, suggesting that assessment of medial-lateral balance control may be particularly important for evaluating the risk of multiple falls. Differences were found between prospective fallers and non-fallers for Romberg Quotient (RQ) anterior-posterior range and root-mean square distance from the mean. Differences between prospective multi-fallers and non-fallers were for eyes closed and RQ anterior-posterior and vector sum magnitude velocity. This suggests that RQ calculations are particularly relevant for elderly fall risk assessments. Measures that changed between ST and DT walking conditions, including non-temporal measures related to movement frequency and abnormal body segment movements, were identified. Increased gait variability under DT conditions was indicated by increased posterior CoP stance path deviations, medial-lateral CoP stance path deviation durations, and CoP stance path coefficient of variation; and decreased Fast Fourier Transform quartiles and ratio of even to odd harmonics. Decreased gait velocity and decreased pelvis and shank acceleration standard deviations (SD) could represent compensatory gait strategies to counter the increased gait variability and thus maintain stability. Differences between prospective fallers and prospective non-fallers were related to movement frequency and variability. Fall risk classification models that used Relief-F feature selection achieved the best performance. With feature selection, the best model for prospective faller classification contained ten features (four pressure-sensing insole features, six left shank accelerometer features) and used a support vector machine classifier. This model achieved an accuracy of 94%, F1 score of 0.923, and Matthew’s Correlation Coefficient (MCC) of 0.866. The posterior pelvis accelerometer provided strong single-sensor performance (83% accuracy, F1 score 0.769, MCC 0.645), although lower than the best multi-sensor model performance, and should be considered if a single-sensor system is necessary to reduce assessment cost and complexity at the point-of-care. Neural networks and support vector machines both achieved strong classification performance and outperformed naïve Bayesian classifiers. Sensor-based models outperformed clinical assessment-based models and posturography-based models for both retrospective and prospective fall risk classification. Wearable sensors provided strong fall risk classification performance and should be considered for point-of-care assessment of elderly fall risk

Differences in sit-to-stand, standing sway and stairs between community-dwelling fallers and non-fallers: a review of the literature.

Background: Falls are extremely common and have a significant impact on an individual’s wellbeing. Individuals who fall often display altered function however to date no synthesis pertaining to the nature of these alterations is available. Such information is important to guide assessment and management strategies. Objectives: To appraise and synthesize literature directly comparing community- dwelling elderly fallers with non-fallers across tasks of sit-to-stand, standing postural sway with eyes open and stairs. Methods: A structured search of Medline, SPORTDicuss, Science Citation Index, OAIster, CINAHL, Academic Search Complete, Science Direct and Scopus databases was conducted in July 2017. Articles were limited to peer-reviewed in the English language comparing elderly community-dwelling fallers to non-fallers. Results: Eight articles were included relating to sit-to-stand, seven for postural sway and one for stairs. Fallers stood from sitting significantly slower, with lower linear velocity and maximum power than non-fallers. This was best observed when arms were not used and when the stand was attempted as quickly as possible. Fallers displayed significantly greater sway path lengths and center of pressure velocity compared with non-fallers, but only when assessed in narrow or near narrow stance. Fallers used less force during stepping up compared with non-fallers. Conclusion: The findings of this review suggest that activities of daily living may be able to discriminate between fallers and non-fallers therefore offering the potential for community based assessment of fallers

Development and validation of a newtest for assessment of plantar-flexor muscle strength in older adults

Background: The Calf-raise (CR) test is often used as a screening tool to assess anklemuscle functioning in clinical practice. Most studies restrict the administration of thistest to the young adult population and, of our knowledge, no study has evaluated thevalidityand reliability of this test with elderly people. Purpose: This study aimed to develop a new field test protocol with a standardizedmeasurement of strength and power in plantar flexor muscles targeted to functionallyindependent older adults, the calf-raise senior (CRS) test, and also evaluate its reliabilityand validity. Patients and methods: Forty-one subjects aged 65 years and older of bothsexesparticipated in five different cross-sectional studies: 1) pilot (n=12); 2) inter and intra-rater agreement (n=12); 3) construct (n=41); 4) criterion validity (n=33); and 5) test–retest reliability (n=41). Different motion parameters were compared in order to defineaspecifically designed protocol for seniors. Two raters evaluated each participant twice, and the results of the same individual were compared between raters and participantstoassess the interrater and intra-rater agreement. The validity and reliability studies involvedthree testing sessions that lasted 2 weeks, including a battery of functional fitness tests, CRS test in two occasions, accelerometry, and strength assessments in an sokineticdynamometer. Results: The CRS test presented an excellent test–retest reliability (intra-class correlationcoefficient [ICC] =0.90, standard error of measurement =2.0) and interrater reliability(ICC=0.93–0.96), as well as a good intra-rater agreement (ICC =0.79–0.84). Participantswithbetter results in the CRS test were younger and presented higher levels of physical activity and functional fitness. A significant association between test results andall strength parameters (isometric, r=0.87, r=0.75; isokinetic, r=0.86, r=0.74; and rateof orcedevelopment, r=0.77, r=0.59) was shown. Conclusion: This study was successful in demonstrating that the CRS test can meet thescientific criteria of validity and reliability. The test can be a good indicator of anklestrength in older adults and proved to discriminate significantly between individualswithimproved functionality and levels of physical activity.Background: The assessment of the plantar-flexors muscle strength in the elderly peopleis of the utmost importance since they are strongly associated o the performanceof fundamental tasks of daily life. Purpose: Our study aims at strengthen the validity of the Calf-Raise Senior (CRS) test byassessing the biomechanical movement pattern of calf muscles in elderly participantswithdifferent functional fitness profiles. Patients and methods: Twenty-six older adults with different levels of functional fitness(FF) and physical activity (PA) participated in this study. CRS test was administered together with a FF battery, accelerometry, strength tests, kinematics and electromyography (EMG). Older adults with the best and worst CRS scores were compared and the associationbetween the scores and EMG pattern of ankle muscles was determined. Results: Older participants with the best CRS scores presented higher levels of FF, PA, strength, power, speed and range of movement, and also a more efficient movement pattern during the test. Subjects who scored more at the CR test demonstratedthepossibly to use a stretch-shortening cycle type of action in the PF muscles to increasepower during the movements. Conclusion: Older adults with different levels of functional fitness can be tratifiedbythemuscular activation pattern of the calf muscles and the scores in CRS test. . This studyreinforced the validity of CRS for evaluating ankle strength and power in elderlyBackground: Mobility significantly depends on the ankle muscles’ strength, whichisparticularly relevant for the performance of daily activities. There are few tools available, with all of the measurement properties tested, to assess ankle strength. Purpose: The purpose of this study was to test the responsiveness of Calf-RaiseSenior Test (CRS) in a sample of elderly participants undergoing a 24 weeks communityexercise program.. Patients and methods: 82 older adults participated in an exercise programandwereassessed with CRS Test and 30-seconds chair stand test (CS) at baseline and at follow- up. Effect size (ES), standardized response mean (SRM) and minimal detectablechange(MDC) measures were calculated for the CRS and CS tests scores. ROC curves analysiswas used to define a cut-off representing the minimally important difference of Calf-RaiseSenior test. Results: Results revealed a small (ES = 0.42) to moderate (SRM = 0.51) responsivenessin plantar-flexion strength and power across time, which was lower than that of CStest (ES = 0.64, SRM = 0.67). The responsiveness of CRS test was more evident in groupsof subjects with lower initial scores. A minimal important difference (MID) of 3.5 repetitionsand a minimal detectable change (MDC) of 4.6 was found for the CRS. Conclusion: Calf-Raise Senior Test is a useful field test to assess elderly ankle function, with moderate responsiveness properties. The cutoff scores of MDC and MIDpresentedin this study can be useful in determining the success of interventions aiming at improvingmobility in senior participants

Wearable Sensors Applied in Movement Analysis

Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges

A new index to assess turning quality and postural stability in patients with Parkinson's disease

Parkinson's disease is a neuro-degenerative disorder characterized by the progressive death of dopamine neurons. This leads to delayed and uncoordinated movements, and impacts on the patients’ motor performance with reduced movement intensity, increased axial rigidity and impaired cadence regulation. Turning provides privileged insights in postural instability and fall prediction, as it is regularly performed during daily activities, requires multi-limb coordination. The objective of this work was to define a Quality of Movement (QoM) index, inferred from inertial data related to turns, and strictly correlated with the patient's motor conditions, postural stability, and stage of the disease. Such a concise representation finds its main application in the remote monitoring of patients during daily activities at home. We have recorded and analyzed 180° turns in 72 patients, using inertial sensors embedded in the smartphone. We have set up an algorithm for binary classification of patients: mild vs. moderate/severe conditions, according to the Hoehn and Yahr scale of disease progression and disability degree. Our QoM index is defined as the a posteriori probability output by this binary classifier. It exhibits high correlation (r = 0.73) with the clinical score of postural stability, as well as with the average of four clinical scores related to movement impairment (r = 0.75). These results, together with the widespread smartphone use, provide a step in the direction of a practical, objective and reliable tool for PD patients remote monitoring in domestic environment