Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms

Abstract

The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user’s sport activity.

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

“…Afsar [ 18 ] achieved an accuracy of 0.75, indicating the proportion of correctly classified instances out of the total. Patalas et al [ 19 ] attained a slightly improved accuracy of 0.8, suggesting a better classification performance. Mekruksavanich et al [ 20 ] demonstrated further enhancement with an accuracy of 0.81, indicating a consistent upward trend.…”

“…Afsar [ 18 ] Achieved a specificity of 0.66, reflecting the ability to capture true negatives effectively. Patalas et al [ 19 ]: Improved the specificity to 0.73, suggesting a better performance in correctly identifying negatives. Mekruksavanich et al [ 20 ] Displayed an enhanced specificity of 0.76, indicating further effective handling of true negatives.…”

“…Afsar [ 18 ] Achieved an F1 score of 0.8, the harmonic mean of precision and recall. Patalas et al [ 19 ] achieved an F1 score of 0.84, showcasing a balanced performance between precision and recall. Mekruksavanich et al [ 20 ] maintained an F1 score of 0.84, suggesting a consistent balance in predictive performance.…”

“…The system uses a convolutional neural network (CNN) with a post-processing block to classify three sports activities: squats, pull-ups, and dips. The system was evaluated on a dataset of 488 instances of the three activities and achieved an accuracy of 0.88-0.92 [19]. Mekruksavanich and Jitpattanakul (2022) proposed a sport-related activity recognition system from wearable sensors using a bidirectional gated recurrent unit (GRU) network.…”

Steganography in IoT: Information Hiding with Joystick and Touch Sensors

“…Input devices collect data from the environment. They may measure temperature [ 46 ], medical parameters [ 47 ], displacement [ 48 ], pH [ 49 ], pressure [ 50 ], humidity [ 51 ], inertia [ 52 ], etc. Output devices broadcast messages to the external world.…”