Paper Info
Title
Vicovr-Based Wireless Daily Activity Recognition And Assessment System For Stroke Rehabilitation
Abstract
Stroke is the leading cause of long-term disability. Stroke patients can recover faster with personalized therapy treatments. This requires both clinical assessments and in-home assessments of daily activities. In this paper, we propose a daily activity recognition and assessment system for stroke patients. Our system is able to classify daily activities in real home environments and quantitatively evaluate upper body motions while preserving privacy by utilizing depth videos. Specifically, our system collects the depth videos and skeletal joint data of daily activities using a VicoVR sensor. It then recognizes and localizes clinically relevant actions from continuous untrimmed depth videos using a customized convolutional de-convolutional network. In addition, it assesses the extent of reach and speed metrics of both hands using skeletal joint data. The system has been tested on simulated cooking videos and real-life cooking videos in various kitchens with different room layouts and light conditions. The action recognition accuracies for simulated and real-life videos can reach 90.9% and 87.5%, respectively. With the valuable assessment feedback of our system, therapists can make better personalized treatments for stroke patients.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621151
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
Field
DocType
VicoVR, Wireless, Android, Daily Activity Recognition, Assessment, Stroke Rehabilitation
Rehabilitation,Activities of daily living,Wireless,Android (operating system),Activity recognition,Computer science,Action recognition,Stroke,Artificial intelligence,Machine learning
Conference
ISSN
Citations 
PageRank 
2156-1125
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Mengxuan Ma111.76
Benjamin J. Meyer232.41
Le Lin300.34
Rachel Proffitt4254.17
Marjorie Skubic51045105.36