A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction

Abstract

Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature.

Real-Time Tracking of Human Neck Postures and Movements

“…K-Nearest Neighbors (KNN): KNN is a supervised learning algorithm, which calculates the nearest distance of a similar object; this is why it is sometimes called a proximity or closeness-finding algorithm [ 46 , 47 ].…”

Ubiquitous Computing in Sports and Physical Activity—Recent Trends and Developments

“…As a result, injury prevention is a major area of research and development of smart monitoring systems. In the new reality of COVID, where fitness is widely performed at home rather than in shared spaces such as traditional gyms, smart portable fitness sites are highly desirable [ 9 ]. The particular system alerts via Android app when inappropriate posture is assumed and actively encourages posture correction.…”

“…Examples from the measurement methods described are in Zhang et al [ 2 ], LeBlanc et al [ 3 ], Rad et al [ 4 ], Liu et al [ 5 ], De Zambotti et al [ 6 ], and Sargent et al [ 7 ]. For methods of processing, current examples are described in Liu et al [ 8 ] and Hannan et al [ 9 ]. Finally, for analysis methods, examples are described in LeBlanc et al [ 3 ], Yanan et al [ 10 ], and Reilly et al [ 11 ].…”

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

“…Geometric features provide information about the relationship between joints in space using linear algebra operations (e.g., angles and distances). These features were used in 21 publications [34], [45], [46], [54], [59], [60], [61], [62], [68], [76], [84], [85], [89], [90], [92], [93], [94], [95], [99], [111], [118]. Kinematic features, which were used in 20 publications, describe the motion of joints [21], [32], [45], [54], [58], [59], [60], [61], [62], [66], [69], [84], [85], [86], [87], [91], [92], [93], [94], [95].…”

“…Primarily, they included statistical techniques, such as Pearson's Correlation and ANOVA, that were used to identify features with the highest correlation to the expected output [49], [69], [70], [84], [91]. Wrapper techniques were used in two publications by applying RL for feature learning [93], [94], and one publication that used forward feature selection [118].…”