Hybrid Sensing for Fatigue Detection Using Wearables and RF

In the field of smart healthcare, wearable and sensing devices are connected to the Internet of Things (IoT) to assess patients within their own homes. Fatigue is a multidimensional experience that can be characterized by exhaustion and reduced physical performance. Monitoring people within their homes to detect slower pace could be a promising method to objectively measure aspects of human fatigue. This article makes use of both a wearable bracelet and radio frequency (RF) sensing to detect simulated fatigue from human activity monitoring. Different activities are collected at a normal pace to represent no fatigue and then repeated at a slower pace to represent fatigue. Artificial intelligence (AI) is used to detect whether there is fatigue present or not regardless of activity taking place as well as identifying which activity took place and whether fatigue is present in said activity. When using the data from both the bracelet and RF sensing, Random Forest and ResNet algorithms achieved 100% in detecting fatigue as opposed to nonfatigue using algorithms. When using only the bracelet, only the Random Forest algorithm was able to achieve 100% accuracy. Using only RF data, 94.80% accuracy was achieved with a convolutional neural network (CNN). When detecting individual activities with fatigue and no fatigue, the Random Forest algorithm achieved an accuracy score of 97.40% using both the bracelet and RF sensing and with only the bracelet data. The CNN was again the best algorithm for RF sensing only with an accuracy score of 89.84%.