Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review

Implantable multilayer interdigitated triboelectric nanogenerator with nano-micro fibrous membrane and embedded switch-controlled capacitor for neck motion energy harvesting

Implantable electronic devices are increasingly recognized as indispensable technologies for a future-oriented humanity. The challenge of power supply is widely recognized as a significant hurdle, requiring interdisciplinary research efforts for its resolution. In this work, a multilayer interdigitated triboelectric nanogenerator (MI-TENG) is introduced, enhancing electrical output by laminating multiple layers of an interdigitated electrode structure. This structure enables multiple electrical output peaks from a single sliding reciprocating motion. For the contact layer, an electrospun nano-micro fibrous poly (methyl methacrylate) membrane is employed, resulting in a sixfold increase in open-circuit voltage (VOC) peak and an 8.2-fold increase in short-circuit current (ISC) peak. Notably, the current output at the end of each operation is amplified by integrating an embedded switch-controlled capacitor into the MI-TENG. The ISC peak reaches 1.51 mA, a 1454.7-fold increase compared to the original ISC peak of 1.038 μA. The voltage is regulated at 2.55 V, and an instantaneous power density of 1.18 W/m2 is achieved at 5 kΩ. The fabricated MI-TENG is applied in neck motion harvesting, where energy is scavenged from various movements on the sternohyoid region. Applications are extended beyond wearable electronics to implantable electronics in a porcine model, demonstrating high potential as a bioenergy harvesting electronic device through real-time energy storage and generation verification.

Constructing Ion-Bridging Structure with Controlled Cracks in Plasticized PVC with Graphene for Highly Sensitive Strain Sensor with a Wide Strain Range

The gauge factor (GF) is a critical parameter for strain sensors, but it faces limitations in achieving high GF values across a wide strain range. This work proposes a novel approach to enhance resistance changes within strains through synergistically combining controlled-crack sizing and an ion-bridging structure. This ion-conductive bridge forms at the interface between graphene and polyvinyl chloride (PVC) gel. Precise management of the crack initiation and propagation on graphene is achieved by controlling adhesion force between graphene and PVC gel. The resulting PVC gel/graphene-based strain sensor featuring this synergistic design exhibits exceptional sensitivity. It achieves GFs of 635 (ε < 40%), 1.5 × 106 (40% < ε < 80%), and 7.8 × 105 (80% < ε < 100%) over a 100% stretching range. This innovative ion-bridging construction enables precise control over bridge connectivity at the interface, mitigating graphene's inherent stretchability limitations and enhancing the GF of PVC gel, thereby enhancing strain sensor performance. The sensor detects bending motions and monitors angles within higher strain ranges, making it suitable for wearable applications in human motion tracking. Furthermore, a PVC-based posture correction system distinguishes various motions, including shoulder band stretching, armband stretching, and even full squats, showcasing its practicality and versatility.

Eco-Friendly Conductive Hydrogels: Towards Green Wearable Electronics

The rapid advancement of wearable electronics has catalyzed the development of flexible, lightweight, and highly conductive materials. Among these, conductive hydrogels have emerged as promising candidates due to their tissue-like properties, which can minimize the mechanical mismatch between flexible devices and biological tissues and excellent electrical conductivity, stretchability and biocompatibility. However, the environmental impact of synthetic components and production processes in conventional conductive hydrogels poses significant challenges to their sustainable application. This review explores recent advances in eco-friendly conductive hydrogels used in healthcare, focusing on their design, fabrication, and applications in green wearable electronics. Emphasis is placed on the use of natural polymers, bio-based crosslinkers, and green synthesis methods to improve sustainability while maintaining high performance. We discuss the incorporation of conductive polymers and carbon-based nanomaterials into environmentally benign matrices. Additionally, the article highlights strategies for improving the biodegradability, recyclability, and energy efficiency of these materials. By addressing current limitations and future opportunities, this review aims to provide a comprehensive understanding of environmentally friendly conductive hydrogels as a basis for the next generation of sustainable wearable technologies.

Artificial intelligence integration in surgery through hand and instrument tracking: a systematic literature review

Objective

This systematic literature review of the integration of artificial intelligence (AI) applications in surgical practice through hand and instrument tracking provides an overview of recent advancements and analyzes current literature on the intersection of surgery with AI. Distinct AI algorithms and specific applications in surgical practice are also examined.

Methods

An advanced search using medical subject heading terms was conducted in Medline (via PubMed), SCOPUS, and Embase databases for articles published in English. A strict selection process was performed, adhering to PRISMA guidelines.

Results

A total of 225 articles were retrieved. After screening, 77 met inclusion criteria and were included in the review. Use of AI algorithms in surgical practice was uncommon during 2013-2017 but has gained significant popularity since 2018. Deep learning algorithms (n = 62) are increasingly preferred over traditional machine learning algorithms (n = 15). These technologies are used in surgical fields such as general surgery (n = 19), neurosurgery (n = 10), and ophthalmology (n = 9). The most common functional sensors and systems used were prerecorded videos (n = 29), cameras (n = 21), and image datasets (n = 7). The most common applications included laparoscopic (n = 13), robotic-assisted (n = 13), basic (n = 12), and endoscopic (n = 8) surgical skills training, as well as surgical simulation training (n = 8).

Conclusion

AI technologies can be tailored to address distinct needs in surgical education and patient care. The use of AI in hand and instrument tracking improves surgical outcomes by optimizing surgical skills training. It is essential to acknowledge the current technical and social limitations of AI and work toward filling those gaps in future studies.

Sensor-based technologies for motion analysis in sports injuries: a scoping review

BACKGROUND

Insightful motion analysis provides valuable information for athlete health, a crucial aspect of sports medicine. This systematic review presents an analytical overview of the use of various sensors in motion analysis for sports injury assessment.

METHODS

A comprehensive search of PubMed/MEDLINE, Scopus, and Web of Science was conducted in February 2024 using search terms related to "sport", "athlete", "sensor-based technology", "motion analysis", and "injury." Studies were included based on PCC (Participants, Concept, Context) criteria. Key data, including sensor types, motion data processing methods, injury and sport types, and application areas, were extracted and analyzed.

RESULTS

Forty-two studies met the inclusion criteria. Inertial measurement unit (IMU) sensors were the most commonly used for motion data collection. Sensor fusion techniques have gained traction, particularly for rehabilitation assessment. Knee injuries and joint sprains were the most frequently studied injuries, with statistical methods being the predominant approach to data analysis.

CONCLUSIONS

This review comprehensively explains sensor-based techniques in sports injury motion analysis. Significant research gaps, including the integration of advanced processing techniques, real-world applicability, and the inclusion of underrepresented domains such as adaptive sports, highlight opportunities for innovation. Bridging these gaps can drive the development of more effective, accessible, and personalized solutions in sports health.

Classifying Food Items During an Eating Occasion: A Machine Learning Approach with Slope Dynamics for Windowed Kinetic Data

BACKGROUND

Wearable devices equipped with a range of sensors have emerged as promising tools for monitoring and improving individuals' health and lifestyle.

OBJECTIVES

Contribute to the investigation and development of effective and reliable methods for dietary monitoring based on raw kinetic data generated by wearable devices.

METHODS

This study uses resources from the NOTION study. A total of 20 healthy subjects (9 women and 11 men, aged 20-31 years) were equipped with two commercial smartwatches during four eating occasions under semi-naturalistic conditions. All meals were video-recorded, and acceleration data were extracted and analyzed. Food recognition on these features was performed using random forest (RF) models with 5-fold cross-validation. The performance of the classifiers was expressed in out-of-bag sensitivity and specificity.

RESULTS

Acceleration along the x-axis and power show the highest and lowest rates of median variable importance, respectively. Increasing the window size from 1 to 5 s leads to a gain in performance for almost all food items. The RF classifier reaches the highest performance in identifying meatballs (89.4% sensitivity and 81.6% specificity) and the lowest in identifying sandwiches (74.6% sensitivity and 72.5% specificity).

CONCLUSIONS

Monitoring food items using simple wristband-mounted wearable devices is feasible and accurate for some foods while unsatisfactory for others. Machine learning tools are necessary to deal with the complexity of signals gathered by the devices, and research is ongoing to improve accuracy further and work on large-scale and real-time implementation and testing.

Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person

Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilized the biomedical signals acquired from the human body's particular organ, cells, or tissues. The human motion intention prediction mechanism plays an essential role in various applications, such as assistive and rehabilitation robots, that execute specific tasks among elders and physically impaired individuals. However, more complications are increased in the human-machine-based interaction techniques, creating more scope for personalized assistance for the human motion intention prediction system. Therefore, in this paper, an Adaptive Hybrid Network (AHN) is implemented for effective human motion intention prediction. Initially, multimodal data like electroencephalogram (EEG)/Electromyography (EMG) signals and sensor measures data are collected from the available data resource. The gathered EEG/EMG signals are then converted into spectrogram images and sent to AH-CNN-LSTM, which is the integration of an Adaptive Hybrid Convolution Neural Network (AH-CNN) with a Long Short-Term Memory (LSTM) network. Similarly, the data details of sensor measures are directly subjected to AH-CNN-Res-LSTM, which is the combination of Adaptive Hybrid CNN with Residual Network and LSTM (Res-LSTM) to get the predictive result. Further, to enhance the prediction, the parameters in both the AH-CNN-LSTM and AH-CNN-Res-LSTM techniques are optimized using the Improved Yellow Saddle Goatfish Algorithm (IYSGA). The efficiency of the implemented model is computed by conducting the comparison experiment of the proposed technique with other standard models. The performance outcome of the developed method outperformed the other traditional methods.

Exploring the potential of electrical bioimpedance technique for analyzing physical activity

Introduction

Exercise physiology investigates the complex and multifaceted human body responses to physical activity (PA). The integration of electrical bioimpedance (EBI) has emerged as a valuable tool for deepening our understanding of muscle activity during exercise.

Method

In this study, we investigate the potential of using the EBI technique for human motion recognition. We analyze EBI signals from the quadriceps muscle and extensor digitorum longus muscle acquired when healthy participants in the range 20-30 years of age performed four lower body PAs, namely squats, lunges, balance walk, and short jumps.

Results

The characteristics of EBI signals are promising for analyzing PAs. Each evaluated PA exhibited unique EBI signal characteristics.

Discussion

The variability in how PAs are executed leads to variations in the EBI signal characteristics, which, in turn, can provide insights into individual differences in how a person executes a specific PA.

Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities to monitor and analyze patients' physical activity. This paper investigates the potential applications of mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using a dataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensive computational methodology is introduced, incorporating traditional digital signal processing, feature extraction in both time and transform domains, and advanced classification techniques. The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. The proposed approach achieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motion patterns by employing features from both the time and frequency domains using a two-layer neural network. These findings demonstrate promising applications of precise monitoring of rehabilitation exercises to increase the probability of successful surgical recovery, highlighting the potential to significantly enhance patient care and treatment outcomes.

Electronic skin based on natural biodegradable polymers for human motion monitoring

The wearability of the flexible electronic skin (e-skin) allows it to attach to the skin for human motion monitoring, which is essential for studying human motion and especially for assessing how well patients are recovering from rehabilitation therapy. However, the use of non-degradable synthetic materials in e-skin may raise skin safety concerns. Natural biodegradable polymers with advantages such as biodegradability, biocompatibility, sustainability, natural abundance, and low cost have the potential to be alternative materials for constructing flexible e-skin and applying them to human motion monitoring. This review summarizes the applications of natural biodegradable polymers in e-skin for human motion monitoring over the past three years, focusing on the discussion of cellulose, chitosan, silk fibroin, gelatin, and sodium alginate. Finally, we summarize the opportunities and challenges of e-skin based on natural biodegradable polymers. It is hoped that this review will provide insights for the future development of flexible e-skin in the field of human motion monitoring.

Carbon Nanotube Sheets/Elastomer Bilayer Harvesting Electrode with Biaxially Generated Electrical Energy

Mechanical energy harvesters made from soft and flexible materials can be employed as energy sources for wearable and implantable devices. However, considering how human organs and joints expand and bend in many directions, the energy generated in response to a mechanical stimulus in only one direction limits the applicability of mechanical energy harvesters. Here, we report carbon nanotube (CNT) sheets/an elastomer bilayer harvesting electrode (CBHE) that converts two-axis mechanical stimulation into electrical energy. The novel microwinkled structure of the CBHE successfully demonstrates an electrochemical double-layer (EDL) capacitance change from biaxial mechanical stimulation, thereby generating electrical power (0.11 W kg-1). Additionally, the low modulus (0.16 MPa) and high deformability due to the elastomeric substrate suggest that the CBHE can be applied to the human body.

Wearables for Monitoring and Postural Feedback in the Work Context: A Scoping Review

Wearables offer a promising solution for simultaneous posture monitoring and/or corrective feedback. The main objective was to identify, synthesise, and characterise the wearables used in the workplace to monitor and postural feedback to workers. The PRISMA-ScR guidelines were followed. Studies were included between 1 January 2000 and 22 March 2023 in Spanish, French, English, and Portuguese without geographical restriction. The databases selected for the research were PubMed®, Web of Science®, Scopus®, and Google Scholar®. Qualitative studies, theses, reviews, and meta-analyses were excluded. Twelve studies were included, involving a total of 304 workers, mostly health professionals (n = 8). The remaining studies covered workers in the industry (n = 2), in the construction (n = 1), and welders (n = 1). For assessment purposes, most studies used one (n = 5) or two sensors (n = 5) characterised as accelerometers (n = 7), sixaxial (n = 2) or nonaxialinertial measurement units (n = 3). The most common source of feedback was the sensor itself (n = 6) or smartphones (n = 4). Haptic feedback was the most prevalent (n = 6), followed by auditory (n = 5) and visual (n = 3). Most studies employed prototype wearables emphasising kinematic variables of human movement. Healthcare professionals were the primary focus of the study along with haptic feedback that proved to be the most common and effective method for correcting posture during work activities.