In smart cities, pervasive sensing and wearable computing techniques are increasingly being employed to monitor and recognize human activities through body sensor networks, which have been widely used in urban safety, healthcare, and manufacturing. However, most researchers regard human activity recognition (HAR) as a high-cost research task requiring a large amount of labeled data, which is often unrealistic in real-world applications. To address this problem, we propose a self-supervised learning framework for HAR (SS-HAR). SS-HAR initially takes the unlabeled data generated by data augmentation as the input of the network, mines the supervised information of the unlabeled data under the effect of self-supervised learning, and uses the obtained backbone network as a feature extractor to extract activity features for subsequent classification. After that, we use part of the labeled data as the training set and extract the activity features using the backbone network for training and fitting the classifier. Then we utilize the rest of the data to verify the feasibility and effectiveness of the proposed self-supervised learning method. We have conducted multiple experiments on three publicly available datasets and one self-collected basketball activity dataset SZU_HAD_Basketball. The experimental results show that the SS-HAR method is able to achieve higher classification accuracy and stability than supervised and semi-supervised methods. Specifically, on the UCI dataset, SS-HAR achieves better classification performance compared to other approaches which improve the classification accuracy by 1% over the supervised method and by 5%-6% over the semi-supervised method respectively. 

A Self-Supervised Human Activity Recognition Approach via Body Sensor Networks in Smart City

Conventional racket sports training highly relies on coaches' knowledge and experience, leading to biases in the guidance. To solve this problem, smart wearable devices based on Internet of Things technology (IoT) have been extensively investigated to support data-driven training. Considerable studies introduced methods to extract valuable information from the sensor data collected by IoT devices. However, the information cannot provide actionable insights for coaches due to the large data volume and high data dimensions. We proposed an IoT + VA framework, Tac-Trainer, to integrate the sensor data, the information, and coaches' knowledge to facilitate racket sports training. Tac-Trainer consists of four components: device configuration, data interpretation, training optimization, and result visualization. These components collect trainees' kinematic data through IoT devices, transform the data into attributes and indicators, generate training suggestions, and provide an interactive visualization interface for exploration, respectively. We further discuss new research opportunities and challenges inspired by our work from two perspectives, VA for IoT and IoT for VA. 

Tac-Trainer: A Visual Analytics System for IoT-based Racket Sports Training

An intelligent sports training system based on Internet of Things (IoT) technology is proposed to build a low-cost, easy-to-use home exercise guidance solution, which can provide reliable exercise guidance when gymnasiums are inaccessible for users. The proposed intelligent system includes an inertial measurement microelectromechanical system with Bluetooth low-energy data transmission technology, a smart dumbbell with an acceleration sensor, an application on the smartphone terminal, and a computing central server in the clouds. Two-loop Kalman filters, dynamic motion segmentation method, and neural network are developed to demonstrate and evaluate the user’s dumbbell exercise modes. Six dumbbell exercise postures and 10 exercise cycles for eight participants are collected for system validation in the experimental study. The experimental results demonstrate that the proposed system can effectively and accurately segment multiple types of dumbbell movements (98.9% accuracy), recognize movements with high reliability (98.3% accuracy), and distinguish standard and non-standard movements (89% accuracy). Finally, this system with an intelligent algorithm software and hardware can be expanded to other similar types of sporting excises.

IoT Motion Tracking System for Workout Performance Evaluation: A Case Study on Dumbbell

A high quality serving ability is crucial for winning a tennis match. Traditionally, tennis training has relied on the use of expensive and difficult to setup multiple highspeed cameras and computer vision technology. A more accessible, cost-effective, and user-friendly solution for tennis training is necessary. A comprehensive motion recognition system based on inertial sensors is proposed for tennis serve training in this article. The proposed system is designed to assist in tennis serve training by utilizing multiple inertial sensors to collect the player’s motion data. It employs small datasets data preprocessing and local attention based convolutional neural network (CNN) method for recognition. The system’s functionality includes landing location prediction, ball speed prediction, and the recognition of faulty actions in the serve action. We conducted comprehensive experiments to evaluate the performance of the proposed model. Compared with similar studies, the system in this article has high performance, with an average error of 0.72 units for the landing point prediction, an average error of 4.36 km/h for the ball speed prediction, and an accuracy rate of 98.2% for faulty actions recognition. Our work enables players to train efficiently and effectively, independent of environmental factors, and improve their movements using quantitative serve results and training recommendations.

Comprehensive Tennis Serve Training System Based on Local attention-based CNN Model

In recent years, machine learning has been utilized in health informatics and sports science. There is a great demand and development potential for combining the Internet of Things (IoT) and artificial intelligence (AI) to be applied to football sports. The conventional teaching and training methods of football sports have limited collection and mining of real raw data using wearable devices, and lack human motion capture and gesture recognition based on sports science theories. In this study, a low-cost AI + IoT system framework is designed to recognize football motion and analyze motion intensity. To reduce the communication delay and the computational resource consumption caused by data operations, a multitask learning model is designed to achieve motion recognition and intensity estimation. The model can perform classification and regression tasks in parallel and output the results simultaneously. A feature extraction scheme is designed in the initial data processing, and feature data augmentation is performed to solve the small sample data problem. To evaluate the performance of the designed football motion recognition algorithm, this paper proposes a data extraction experimental scheme to complete the data collection of different motions. Model validation is performed using three publicly available datasets, and the features learning strategies are analyzed. Finally, experiments are conducted on the collected football motion datasets and the experimental results show that the designed multitask model can perform two tasks simultaneously and can achieve high computational efficiency. The multitasking single-layer long short-term memory (LSTM) network with 32 neural units can achieve the accuracy of 0.8372, F1 score of 0.8172, mean average precision (mAP) of 0.7627, and mean absolute error (MAE) of 0.6117, while the multitasking single-layer LSTM network with 64 neural units can achieve the accuracy of 0.8407, F1 score of 0.8132, mAP of 0.7728, and MAE of 0.5966.

https://downloads.hindawi.com/journals/abb/2023/2354728.pdf

Lightweight Football Motion Recognition and Intensity Analysis Using Low-Cost Wearable Sensors

Motion tracking in different fields (medical, military, film, etc.) based on microelectromechanical systems (MEMS) sensing technology has been attracted by world's leading researchers and engineers in recent years; however, there is still a lack of research covering the sports field. In this study, we propose a new AIoT (AI + IoT) paradigm for next-generation foot-driven sports (soccer, football, takraw, etc.) training and talent selection. The system built is cost-effective and easy-to-use and requires much fewer computational resources than traditional video-based analysis on monitoring motions of players during training. The system built includes a customized wireless wearable sensing device (WWSDs), a mobile application, and a data processing interface-based cloud with an ankle attitude angle analysis model. Eleven right-foot male participators wore the WWSD on their ankle while each performed 20 instances of different actions in a formal soccer field. The experimental outcome demonstrates the proposed motion tracking system based on AIoT and MEMS sensing technologies capable of recognizing different motions and assessing the players' skills. The talent selection function can partition the elite and amateur players at an accuracy of 93%. This intelligent system can be an emerging technology based on wearable sensors and attain the experience-driven to data-driven transition in the field of sports training and talent selection and can be easily extended to analyze other foot-related sports motions (e.g., taekwondo, tumble, and gymnastics) and skill levels.

/pdf/an-iot-based-motion-tracking-system-for-next-generation-foot-10bww20vcd.pdf

An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection.

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An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection.