Reliable and robust detection of freezing of gait episodes with wearable electronic devices

A wearable wireless sensing system for assisting patients affected by Parkinson's disease is proposed. It uses integrated micro-electro-mechanical inertial sensors able to recognize the episodes of involuntary gait freezing. The system operates in real time and is designed for outdoor and indoor applications. Standard tests were performed on a noticeable number of patients and healthy persons and the algorithm demonstrated its reliability and robustness respect to individual specific gait and postural behaviors. The overall performances of the system are excellent with a specificity higher than 97%

Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk

Asymmetry is a cardinal symptom of gait post-stroke that is targeted during rehabilitation. Technological developments have allowed accelerometers to be a feasible tool to provide digital gait variables. Many acceleration-derived variables are proposed to measure gait asymmetry. Despite a need for accurate calculation, no consensus exists for what is the most valid and reliable variable. Using an instrumented walkway (GaitRite) as the reference standard, this study compared the validity and reliability of multiple acceleration-derived asymmetry variables. Twenty-five post-stroke participants performed repeated walks over GaitRite whilst wearing a tri-axial accelerometer (Axivity AX3) on their lower back, on two occasions, one week apart. Harmonic ratio, autocorrelation, gait symmetry index, phase plots, acceleration, and jerk root mean square were calculated from the acceleration signals. Test–retest reliability was calculated, and concurrent validity was estimated by comparison with GaitRite. The strongest concurrent validity was obtained from step regularity from the vertical signal, which also recorded excellent test–retest reliability (Spearman’s rank correlation coefficients (rho) = 0.87 and Intraclass correlation coefficient (ICC21) = 0.98, respectively). Future research should test the responsiveness of this and other step asymmetry variables to quantify change during recovery and the effect of rehabilitative interventions for consideration as digital biomarkers to quantify gait asymmetry

Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation

This paper presents a novel autonomous quality metric to quantify the rehabilitations progress of subjects with knee/hip operations. The presented method supports digital analysis of human gait patterns using smartphones. The algorithm related to the autonomous metric utilizes calibrated acceleration, gyroscope and magnetometer signals from seven Inertial Measurement Unit attached on the lower body in order to classify and generate the grading system values. The developed Android application connects the seven Inertial Measurement Units via Bluetooth and performs the data acquisition and processing in real-time. In total nine features per acceleration direction and lower body joint angle are calculated and extracted in real-time to achieve a fast feedback to the user. We compare the classification accuracy and quantification capabilities of Linear Discriminant Analysis, Principal Component Analysis and Naive Bayes algorithms. The presented system is able to classify patients and control subjects with an accuracy of up to 100\%. The outcomes can be saved on the device or transmitted to treating physicians for later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed autonomous quality metric solution bears great potential to be used and deployed to support digital healthcare and therapy.Comment: 5 Page

Tibial acceleration-based prediction of maximal vertical loading rate during overground running : a machine learning approach

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 +/- 2.04 BW.s(-1), mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 +/- 7.90 BW.s(-1) (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA

Identification of Persons and Several Demographic Features based on Motion Analysis of Various Daily Activities using Wearable Sensors

In recent years, there has been an increasing interest in using the capabilities of wearable sensors, including accelerometers, gyroscopes and magnetometers, to recognize individuals while undertaking a set of normal daily activities. The past few years have seen considerable research exploring person recognition using wearable sensing devices due to its significance in different applications, including security and human-computer interaction applications. This thesis explores the identification of subjects and related multiple biometric demographic attributes based on the motion data of normal daily activities gathered using wearable sensor devices. First, it studies the recognition of 18 subjects based on motion data of 20 daily living activities using six wearable sensors affixed to different body locations. Next, it investigates the task of classifying various biometric demographic features: age, gender, height, and weight based on motion data of various activities gathered using two types of accelerometers and one gyroscope wearable sensors. Initially, different significant parameters that impact the subjects' recognition success rates are investigated. These include studying the performance of the three sensor sources: accelerometer, gyroscope, and magnetometer, and the impact of their combinations. Furthermore, the impact of the number of different sensors mounted at different body positions and the best body position to mount sensors are also studied. Next, the analysis also explored which activities are more suitable for subject recognition, and lastly, the recognition success rates and mutual confusion among individuals. In addition, the impact of several fundamental factors on the classification performance of different demographic features using motion data collected from three sensors is studied. Those factors include the performance evaluation of feature-set extracted from both time and frequency domains, feature selection, individual sensor sources and multiple sources. The key findings are: (I) Features extracted from all three sensor sources provide the highest accuracy of subjects recognition. (2) The recognition accuracy is affected by the body position and the number of sensors. Ankle, chest, and thigh positions outperform other positions in terms of the recognition accuracy of subjects. There is a depreciating association between the subject classification accuracy and the number of sensors used. (3) Sedentary activities such as watching tv, texting on the phone, writing with a pen, and using pc produce higher classification results and distinguish persons efficiently due to the absence of motion noise in the signal. (4) Identifiability is not uniformly distributed across subjects. (5) According to the classification results of considered biometric features, both full and selected features-set derived from all three sources of two accelerometers and a gyroscope sensor provide the highest classification accuracy of all biometric features compared to features derived from individual sensors sources or pairs of sensors together. (6) Under all configurations and for all biometric features classified; the time-domain features examined always outperformed the frequency domain features. Combining the two sets led to no increase in classification accuracy over time-domain alone