A Smart Glove to Track Fitness Exercises by Reading Hand Palm

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.

Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review

“…Overall, eleven distinct wearable/IoT devices types have been evaluated for fitness assessment. The examined studies were conducted using a glove [ 38 , 57 ], wristband [ 39 , 40 , 41 , 46 , 49 , 52 , 53 ], calf band [ 42 ], bicycle [ 44 ], waistband [ 45 ], chest band [ 46 ], smartwatch [ 47 , 54 ], smartphone attached to belt [ 48 ], T-shirt [ 50 ], upper torso strap [ 51 ] and bracelet [ 53 ]. Two studies did not report any results regarding the use of wearable/IoT devices for fitness assessment [ 44 , 47 ].…”

“…Regarding criterion B, reducing the overall power consumption of wearable/IoT technologies is crucial to achieving higher maturity levels. However, only two of the examined works [ 38 , 42 ] evaluated the autonomy, which can be observed by the fact that the majority of the evaluated works are still at the prototype stage. Additionally, the convergence towards the design of Application Specific Integrated Circuits (ASICs) will help to reduce the overall power consumption at the same time that integration and miniaturization will pave the way to less invasive wearable and IoT devices in sports.…”

“…Regarding criterion C, the majority of the examined works use communication standards designed to operate in Local Area Networks (LAN) using Wi-Fi [ 47 , 48 , 49 ] and Zigbee [ 53 ]) protocols, or in a Personal Area Network (PAN) using the Bluetooth [ 41 , 46 , 53 , 54 ] and Bluetooth Low-Energy (BLE) [ 38 , 40 , 44 , 49 , 52 ]) communication protocols. Only one work uses a Wide Area Network (WAN), i.e., 4G mobile communications [ 56 ].…”

Lightweight Authenticated-Encryption Scheme for Internet of Things Based on Publish-Subscribe Communication

“…The fitness of humans can be monitored using IoT devices based on daily activities. For example, the number of steps taken and the amount of exercises done can be measured by using fitness trackers [14]. This suggests that IoT applications will revolutionize health-care industries in monitoring and fitness activities.…”

Heterogeneous Non-Local Fusion for Multimodal Activity Recognition

“…Initial work focused on recognition from traditional machine learning approaches, such as decision trees, support vector machines and random forests [13,16,21,40,50]. For tasks such as tracking in sports activities, intensity and count repetition estimation even suffices [2,56]. Nonetheless, deep learning solutions have achieved state-of-the-art performance in the context of activity recognition, where training data is represented by temporal sensor recordings.…”