Sarcopenia, characterized by progressive loss of muscle mass and function, significantly impacts the quality of life in aging populations. Early detection and personalized intervention are crucial yet challenging due to the limited accessibility and scalability of traditional diagnostic methods. Building upon our previous work on gait-based assessment, this study presents a novel framework for early sarcopenia-prone risk detection using the five-time sit-to-stand (5TSTS) test, embodying Healthcare Industry 5.0’s vision of mass personalization with human-centered technology. Utilizing the Internet of Things (IoT)-enabled wearable inertial measurement units (IMUs) and advanced analytics, our system segments 5TSTS into four biomechanically significant submotions [standing up (StU), standing transition (StT), sitting down (SiD), and sitting transition (SiT)]. This granular segmentation allows mass personalization in diagnostic evaluations by capturing individual-specific biomechanical profiles via wavelet-based feature extraction and machine learning (ML) techniques. Our framework employs big data analytics tools, including the extreme gradient boosting (XGBoost)-based feature selection and support vector machine synthetic minority oversampling technique (SVMSMOTE), to handle class imbalance and optimize individualized predictive accuracy. Tested on data from 52 elderly participants (aged 65–84 years), the system achieves outstanding personalized classification accuracy—up to 97.97% for multiclass risk stratification and 99.28% for binary (healthy versus sarcopenia-prone) classification—highlighting its potential for precise, patient-specific clinical decision-making. Furthermore, the wireless capability of our IoT-enabled wearable IMUs, coupled with minimal setup requirements, facilitates seamless data integration into cloud-based healthcare systems. This integration supports real-time remote monitoring and personalized health management. By leveraging advanced sensing, analytics, and connectivity technologies, our approach significantly advances personalized, accessible, and scalable sarcopenia-prone risk assessment, thereby contributing directly to the vision of Healthcare Industry 5.0.

Sarcopenia is a progressive condition characterized by age-related losses in muscle mass and strength, and irreversible in its advanced stages. While sarcopenia negatively impacts daily living, accurately, quickly and economically assessing its effects can be challenging due to individual variability in activity levels. This study introduced a novel approach for assessing the risk of sarcopenia-prone using machine learning and wearable Internet of Things (IoT) sensors. A total of 53 community-dwelling older adults aged 65+ underwent gait analysis using dual sensors. Nineteen gait features were extracted from each cycle and used to train classification algorithms to categorize participants as healthy, risk level 1, risk level 2, or risk level 3 for sarcopenia. Binary classification of healthy versus sarcopenic-prone achieved 97.41% accuracy on average, while four-class classification averaged 94.67%. Notably, the research discovered worsening gait symmetry with increasing sarcopenia-prone severity. These results indicate IoT sensor-assessed gait may serve as a sensitive indicator for daily sarcopenia-prone screening. Accurate assessment of sarcopenia-prone individuals can be achieved through only a 4-m walking test, significantly reducing the burden for older adults. This approach offers a cost-effective, convenient, and accurate method for early sarcopenia risk detection and intervention, potentially improving quality of life for older adults. This system could also aid in creating widely applicable monitoring products for assessing sarcopenia risk, supporting IoT, and thereby enabling early identification and intervention for individuals at risk of this condition.

Throat microphones show potential as wearable IoT sensors for voice and larynx movement recognition. By picking up vocal fold vibrations directly from the human throat, these can detect speech in noisy or windy environments where traditional microphones fail. Recent studies have investigated soft throat microphones due to their conformable fit with human skin. However, previous work has focused primarily on speaker recognition rather than the speech recognition capabilities of these sensors. This paper presents a flexible sponge-structured throat microphone that can accurately detect the fundamental frequency (F0) and F0 contour of human speech. Comparison with commercial contact microphones and air microphones demonstrates the proposed IoT throat microphone’s ability to capture vocal fold vibrations. While high throat vibration frequencies are damped by biological tissue filtering, the sensor can still achieve 89.80% accuracy in classifying 15 English words and 97.84% for 15 Chinese Mandarin words using signals lowpass filtered at 500Hz. Beyond voice recognition, a non-verbal “speaking bandage” system was also built to map throat movements like swallowing, coughing and mouth opening to words in real-time. This novel soft sensor demonstrates promise as an effective wearable for advanced larynx movement and voice recognition via IoT technologies. Potential applications include augmentative communication, rehabilitation, and human-computer interaction – opening new directions for assistive technologies powered by the subtleties of human speech production.

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes’ sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players’ serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player’s skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes’ training.

More than 100 million animals are used in research, education, and testing per year, and 95% of them are mice and rats. We have developed wireless artificial intelligent (AI)-powered Internet of Things (IoT) sensors (AIIS) for laboratory mice motion recognition utilizing embedded microinertial measurement units (uIMUs)—a new sensing platform fills an important research gap of monitoring behaviors of many laboratory mice in parallel. We have demonstrated a wireless IoT sensor that could be attached and carried by mice (i.e., animals that typically weigh only 20 g and with body length of ~10 cm) and used the collected motion data to recognize five common mice behaviors (e.g., sleeping, walking, rearing, digging, and shaking) in cages with an accuracy of ~76%. For comparison, current commercial video-based tracking systems that track animal behaviors in real time can reach only 70% accuracy and with limited number of parallelly tracked animals. Furthermore, several machine learning algorithms were explored to solve the imbalanced sample data problem, which allowed the accuracy of mice motion recognition to improve from ~48% to ~76% (if shaking is removed from classification, an average accuracy of 86.46% could be achieved). Less frequent mice behaviors, such as rearing, digging, grooming, drinking, and scratching, could also be recognized at an average accuracy of 96.35%. We believe this work has the potential to revolutionize animal behavioral tracking methodology by offering a solution for large batches of simultaneous small animal motion tracking and AI-based behavior recognition.

We propose an Internet of Things (IoT) framework for next-generation racket sports training. To validate its performance, a wireless wearable sensing device (WSD) based on microelectromechanical systems motion sensors was used to recognize different badminton strokes and classify skill levels from different badminton players. The system includes a customized sensor node for data collection, a mobile app, and a cloud-based data processing unit. The WSD developed is low-cost, easy-to-use, and computationally efficient compared to video-based methods for analyzing badminton strokes. It offers the advantage of dynamic monitoring of multiple players in indoor and outdoor environments. In this paper, we present the hardware design, mobile software implementation, and data processing algorithms of the system. Twelve right-handed male subjects wore the WSD on their wrists while each performed 30 trials of different strokes in a real badminton court. The results show that our system is capable of recognizing three different actions, i.e.,smashes,clears, anddrops, with an accuracy rate of 97%. The skill assessment function can differentiate between professional, subelite, and amateur players from their stroke performance. This IoT framework aims to change the way of racket sports training from experience-driven (subjective) to data-driven (objective), and which can be easily extended to analyze the motions and skill levels of players in other racket sports (e.g., tennis, table tennis, and squash) for training and/or practice.