The accurate and reliable detection or prediction of freezing of gaits (FOG) is important for fall prevention in Parkinson's Disease (PD) and studying the physiological transitions during the occurrence of FOG. Integrating both commercial and self-designed sensors, a protocal has been designed to acquire multimodal physical and physiological information during FOG, including gait acceleration (ACC), electroencephalogram (EEG), electromyogram (EMG), and skin conductance (SC). Two tasks were designed to trigger FOG, including gait initiation failure and FOG during walking. A total number of 12 PD patients completed the experiments and produced a total length of 3 hours and 42 minutes of valid data. The FOG episodes were labeled by two qualified physicians. Each unimodal data and combinations have been used to detect FOG. Results showed that multimodal data benefit the detection of FOG. Among unimodal data, EEG had better discriminative ability than ACC and EMG. However, the acquisition of EEG are more complicated. Multimodal motional and electrophysiological data can also be used to study the physiological transition process during the occurrence of FOG and provide personalised interventions.
ECE496Y Final Report: Edge Machine Learning for Detecting Freezing of Gait in Parkinson's Patients
帕金森氏病是一种常见的神经系统疾病，伴随着许多运动缺陷症状。在这个项目中，我们开发了一个带有上传的边缘机器学习算法的设备，可以检测帕金森患者步态症状的冻结。该算法在使用来自10名患者的数据进行验证时达到了83.7%的准确度。该模型部署在微控制器Arduino Nano 33 BLE Sense Board模型中，并通过从计算机传输到微控制器的数据进行实时操作验证。
Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data
University of Exeter（埃克塞特大学）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Cambridge（剑桥大学）；University of Exeter Medical School（埃克塞特大学医学院）；NIHR Exeter BRC（英国国立卫生研究院埃克塞特生物医学研究中心）
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）
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）
Deep Learning Techniques for Improving Digital Gait Segmentation
University of Padova（帕多瓦大学）；National Centre of Neurology and Psychiatry (NCNP)（国立精神·神经医疗研究中心）；Newcastle University（纽卡斯尔大学）；Newcastle upon Tyne Hospitals NHS Foundation Trust（纽卡斯尔医院NHS基金会信托）
Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning
The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen Youjia Innov Tech Co., Ltd.（深圳优佳创新科技有限公司）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳－香港产学研研究院）
