Abstract
Medical studies have intensively demonstrated that sports activity can enhance both the mental and the physical health of practitioners. In recent years, fitness activity became the most common way to motivate and engage people in sports activity. Recently, there have been multiple attempts to elaborate on the “ideal” IoT-based solution to track and assess these fitness activities. Most fitness activities (except aerobic activities like running) involve one or multiple interactions between the athlete’s hand palms and body or between the hand palms and the workout materials. In this work, we present our idea to exploit these biomechanical interactions of the hand palms to track fitness activities via a smart glove. Our smart glove-based system integrates force-sensitive resistor (FSR) sensors into wearable fitness gloves to identify and count fitness activity, by analyzing the time series of the pressure distribution in the hand palms observed during fitness sessions. To assess the performance of our proposed system, we conducted an experimental study with 10 participants over 10 common fitness activities. For the user-dependent activity recognition case, the experimental results showed 88.90% of the F score for overall activity recognition. The result of leave-one-participant-out cross-validation showed an F score ranging from 58.30% to 100%, with an average of 82.00%. For the exercise repetition count, the system achieved an average counting error of 9.85%, with a standard deviation of 1.38.
Highlights
The last decades have seen a growing interest in systems that encourage and support people with regard to the regular practice of physical activity in general and fitness activity in particular
The core contributions of this paper are as follows: (i) Novel wearable sensing approach: we introduce a new resistive pressure glove that uses force-sensitive resistor (FSR) sensors suitably mounted in the inside part of a sports glove to read real-time biomechanical information from the hand palm to recognize and count fitness activity (ii) Single activity detection: by analyzing the pressure distribution applied over the entire palm surface during a workout session, we can recognize most exercises of the following 4 different groups of fitness training (a) flexibility training
For each exercise set performed by a user, we characterize the exercise as a single signal, denoted by weight t, obtained by averaging the 16channel values over the time
Summary
The last decades have seen a growing interest in systems that encourage and support people with regard to the regular practice of physical activity in general and fitness activity in particular. With the recent development of wearable computing and sensor technology, a wide range of ubiquitous systems has been developed by researchers and commercial industries to support and motivate people towards physical activity. These systems are designed to track physical activity, evaluate user results, and support decision-making to improve user performances. Each of these systems was designed to only track and evaluate a specific set of physical activities
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