Freezing of gait is a Parkinson's Disease symptom that episodically inflicts a patient with the inability to step or turn while walking. While medical experts have discovered various triggers and alleviating actions for freezing of gait, the underlying causes and prediction models are still being explored today. Current freezing of gait prediction models that utilize machine learning achieve high sensitivity and specificity in freezing of gait predictions based on time-series data; however, these models lack specifications on the type of freezing of gait events. We develop various deep learning models using the transformer encoder architecture plus Bidirectional LSTM layers and different feature sets to predict the three different types of freezing of gait events. The best performing model achieves a score of 0.427 on testing data, which would rank top 5 in Kaggle's Freezing of Gait prediction competition, hosted by THE MICHAEL J. FOX FOUNDATION. However, we also recognize overfitting in training data that could be potentially improved through pseudo labelling on additional data and model architecture simplification.
Freezing of gait (FoG) is a common gait disability in Parkinson's disease, that usually appears in its advanced stage. Freeze episodes are associated with falls, injuries, and psychological consequences, negatively affecting the patients' quality of life. For detecting FoG episodes automatically, a highly accurate detection method is necessary. This paper presents an approach for detecting FoG episodes utilizing a deep recurrent neural network (RNN) on 3D-accelerometer measurements. We investigate suitable features and feature combinations extracted from the sensors' time series data. Specific…
University of Toronto（多伦多大学）；Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所）
This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a squeeze and excitation convolutional neural network (CNN). In a validation using a public dataset, the prototype developed achieved a FoG detection sensitivity of 88.8% and an F1 score of 85.34%, using less than 20 k tra…
Improvement of Performance in Freezing of Gait detection in Parkinsons Disease using Transformer networks and a single waist worn triaxial accelerometer
Universidad Politécnica de Madrid（马德里理工大学）；University of Minho（米尼奥大学）；Politecnico di Torino（都灵理工大学）
Bi-cephalic self-attended model to classify Parkinson's disease patients with freezing of gait
University of South Dakota（南达科他大学）；LMU University Hospital, Ludwig-Maximilians-Universität München（路德维希-马克西米利安-慕尼黑大学LMU大学医院）；Medical University of South Carolina（南卡罗来纳医科大学）
FM-FoG: A Real-Time Foundation Model-based Wearable System for Freezing-of-Gait Mitigation
Department of Computer Science, William \& Mary Virginia USA；Department of Neurology, Virginia Commonwealth University Virginia USA；School of Nursing, Virginia Commonwealth University Virginia USA；Department of Computer Science, William \& Mary；Department of Neurology, Virginia Commonwealth University；School of Nursing, Virginia Commonwealth University
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；Bangladesh University of Engineering and Technology（孟加拉国工程与技术大学）；Khulna University of Engineering and Technology（库尔纳工程与技术大学）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）
Hitachi (China) Research & Development Corporation（日立（中国）研究开发有限公司）
Fall is a leading cause of death which suffers the elderly and society. Timed Up and Go (TUG) test is a common tool for fall risk assessment. In this paper, we propose a method for predicting TUG score from gait characteristics extracted from video with computer vision and machine learning technologies. First, 3D pose is estimated from video captured with 2D and 3D cameras during human motion and then a group of gait characteristics are computed from 3D pose series. After that, copula entropy is used to select those characteristics which are mostly associated with TUG score. Finally, the selec…
Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability. Therefore, this paper investigates whether a structured machine learning approach can achieve a more accurate prediction of running gait event timings from tibial accelerometry…
Gait2Hip-60: A Unified Deep Learning Benchmark for Predicting Hip Muscle Forces and Joint Moments from Multi-Cadence Gait Kinematics
Capital University of Physical Education and Sports（首都体育学院）；Beijing Institute of Technology（北京理工大学）；Beijing Key Laboratory of Interdisciplinary Intelligent Technologies of Sports, Medicine and Engineering（北京体育医学与工程交叉智能技术重点实验室）
Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries
Wake Forest School of Medicine（维克森林医学院）；Atrium Health Musculoskeletal Institute（Atrium Health肌肉骨骼研究所）；Wake Forest University School of Medicine（维克森林大学医学院）
Hitachi (China) Research & Development Corporation（日立（中国）研究开发有限公司）
As the world ages, elderly care becomes a big concern of the society. To address the elderly's issues on dementia and fall risk, we have investigated smart cognitive and fall risk assessment with machine learning methodology based on the data collected from finger tapping test and Timed Up and Go (TUG) test. Meanwhile, we have discovered the associations between cognition and finger motion from finger tapping data and the association between fall risk and gait characteristics from TUG data. In this paper, we jointly analyze the finger tapping and gait characteristics data with copula entropy. …
Learn to Swim: Data-Driven LSTM Hydrodynamic Model for Quadruped Robot Gait Optimization
Zhejiang University（浙江大学）；FSI lab, Westlake University（西湖大学FSI实验室）；Xinyang Normal University（新阳师范学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Predicting Onflow Parameters Using Transfer Learning for Domain and Task Adaptation
German Aerospace Center (DLR) - Institute of Aerodynamics and Flow Technology, Center for Computer Applications in AeroSpace Science and Engineering（德国航空航天中心（DLR）- 流体力学与流动技术研究所，航空科学与工程计算机应用中心）；German Aerospace Center (DLR) - Institute of Aerodynamics（德国航空航天中心（DLR）- 流体力学与流动技术研究所）；Flow Technology, Center for Computer Applications in AeroSpace Science（流动技术，航空科学计算机应用中心）
Multi-Task Learning for Sparsely-Labeled Time Series: A Case Study on Cold-Hardiness Modeling
School of Electrical Engineering and Computer Science, Oregon State University（俄勒冈州立大学电气与计算机工程学院）；School of Electrical Engineering and Computer Science, Washington State University（华盛顿州立大学电气与计算机工程学院）；Irrigated Agriculture Research and Extension Center, Washington State University（华盛顿州立大学灌溉农业研究与推广中心）
