VicoVR-Based Wireless Daily Activity Recognition and Assessment System for Stroke Rehabilitation
VicoVR-Based Wireless Daily Activity Recognition and Assessment System for Stroke Rehabilitation
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DOI：
10.1109/bibm.2018.8621151
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Mengxuan Ma;Benjamin J. Meyer;Le Lin;Rachel Proffitt;M. Skubic
中科院分区：
文献类型：
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作者：
Mengxuan Ma;Benjamin J. Meyer;Le Lin;Rachel Proffitt;M. Skubic
中风是导致长期残疾的主要原因。中风患者可以通过个性化治疗更快地恢复。这需要临床评估和日常活动的家庭评估。在本文中，我们提出了一个日常活动识别和评估系统，中风患者。我们的系统能够对真实的家庭环境中的日常活动进行分类，并定量评估上身运动，同时通过利用深度视频保护隐私。具体来说，我们的系统使用VicoVR传感器收集日常活动的深度视频和骨骼关节数据。然后，它使用定制的卷积去卷积网络从连续的未修剪深度视频中识别和定位临床相关动作。此外，它还使用骨骼关节数据评估双手的伸展范围和速度指标。该系统已在不同房间布局和光线条件的各种厨房中的模拟烹饪视频和真实烹饪视频上进行了测试。模拟和真实视频的动作识别准确率分别达到90.9%和87.5%。有了本系统宝贵的评估反馈，治疗师可以更好地为脑卒中患者制定个性化的治疗方案。
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.