Development and validation of a neural network for adaptive gait cycle detection from kinematic data

(1) Background: Instrumented gait analysis is a tool for quantification of the different aspects of the locomotor system. Gait analysis technology has substantially evolved over the last decade and most modern systems provide real-time capability. The ability to calculate joint angles with low delays paves the way for new applications such as real-time movement feedback, like control of functional electrical stimulation in the rehabilitation of individuals with gait disorders. For any kind of therapeutic application, the timely determination of different gait phases such as stance or swing is crucial. Gait phases are usually estimated based on heuristics of joint angles or time points of certain gait events. Such heuristic approaches often do not work properly in people with gait disorders due to the greater variability of their pathological gait pattern. To improve the current state-ofthe- art, this thesis aims to introduce a data-driven approach for real-time determination of gait phases from kinematic variables based on long short-term memory recurrent neural networks (LSTM RNNs). (2) Methods: In this thesis, 56 measurements with gait data of 11 healthy subjects, 13 individuals with incomplete spinal cord injury and 10 stroke survivors with walking speeds ranging from 0.2 m s up to 1 m s were used to train the networks. Each measurement contained kinematic data from the corresponding subject walking on a treadmill for 90 seconds. Kinematic data was obtained by measuring the positions of reflective markers on body landmarks (Helen Hayes marker set) with a sample rate of 60Hz. For constructing a ground truth, gait data was annotated manually by three raters. Two approaches, direct regression of gait phases and estimation via detection of the gait events Initial Contact and Final Contact were implemented for evaluation of the performance of LSTM RNNs. For comparison of performance, the frequently cited coordinate- and velocity-based event detection approaches of Zeni et al. were used. All aspects of this thesis have been implemented within MATLAB Version 9.6 using the Deep Learning Toolbox. (3) Results: The mean time difference between events annotated by the three raters was −0.07 ± 20.17ms. Correlation coefficients of inter-rater and intra-rater reliability yielded mainly excellent or perfect results. For detection of gait events, the LSTM RNN algorithm covered 97.05% of all events within a scope of 50ms. The overall mean time difference between detected events and ground truth was −11.62 ± 7.01ms. Temporal differences and deviations were consistently small over different walking speeds and gait pathologies. Mean time difference to the ground truth was 13.61 ± 17.88ms for the coordinate-based approach of Zeni et al. and 17.18 ± 15.67ms for the velocity-based approach. For estimation of gait phases, the gait phase was determined as a percentage. Mean squared error to the ground truth was 0.95 ± 0.55% for the proposed algorithm using event detection and 1.50 ± 0.55% for regression. For the approaches of Zeni et al., mean squared error was 2.04±1.23% for the coordinate-based approach and 2.24±1.34% for the velocity-based approach. Regarding mean absolute error to the ground truth, the proposed algorithm achieved a mean absolute error of 1.95±1.10% using event detection and one of 7.25 ± 1.45% using regression. Mean absolute error for the coordinate-based approach of Zeni et al. was 4.08±2.51% and 4.50±2.73% for the velocity-based approach. (4) Conclusion: The newly introduced LSTM RNN algorithm offers a high recognition rate of gait events with a small delay. Its performance outperforms several state-of-theart gait event detection methods while offering the possibility for real-time processing and high generalization of trained gait patterns. Additionally, the proposed algorithm is easy to integrate into existing applications and contains parameters that self-adapt to individuals’ gait behavior to further improve performance. In respect to gait phase estimation, the performance of the proposed algorithm using event detection is in line with current wearable state-of-the-art methods. Compared with conventional methods, performance of direct regression of gait phases is only moderate. Given the results, LSTM RNNs demonstrate feasibility regarding event detection and are applicable for many clinical and research applications. They may be not suitable for the estimation of gait phases via regression. For LSTM RNNs, it can be assumed, that with a more optimal configuration of the networks, a much higher performance is achieved

A double closed loop to enhance the quality of life of Parkinson's disease patients: REMPARK system

This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait. The belt-worn sensor analyzes patient's movement through real-time learning algorithms that were developed on the basis of a database previously collected from 93 PD patients. The second loop consists in disease management based on the data collected during long periods and that enables neurologists to tailor medication of their PD patients and follow the disease evolution. REMPARK system is going to be tested in 40 PD patients in Spain, Ireland, Italy and Israel. This paper describes the approach followed to obtain this system, its components, functionalities and trials in which the system will be validated.Postprint (published version

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.Peer ReviewedPostprint (published version