Posture Monitoring Research Articles

Posture and Pain: Beliefs and Attitudes of Patients With Chronic Low Back Pain.

Posture is often associated with pain by patients, professionals, and health information channels. However, the extent to which patients perceive the relationship between posture and chronic pain is not well understood. This study aimed to investigate and understand the beliefs and attitudes related to posture among patients with chronic low back pain. This is a qualitative descriptive study that investigated individuals with chronic low back pain, who were on the waiting list for physiotherapy. Data were collected through individual and semi-structured interviews. Thematic content analysis was used to analyse the data. Fifteen adults (11 women and 4 men) were interviewed. Three themes were identified and mapped within the dimensions proposed by the Common Sense Model: (1) Identity, (2) Cause and (3) Control. Participants' statements about posture predominantly followed the biomedical model, with participants holding a mental representation of a 'correct' posture necessary for maintaining a healthy spine. They associated perceived incorrect postures or positions with the cause or worsening of pain. To control symptoms, participants believed that constant care and monitoring of posture in various situations were necessary. Misconceptions about ideal posture and its relationship to chronic pain can lead individuals with low back pain to engage in constant monitoring and avoid certain movements and positions in daily activities. These beliefs may negatively impact prognosis, contribute to the maintenance of symptoms, and affect adherence to treatments.

Automatic identification of the endangered Hawksbill sea turtle behavior using deep learning and cross-species transfer

The accelerometer, an onboard sensor, enables remote monitoring of animal posture and movement, allowing researchers to deduce behaviors. Despite the automated analysis capabilities provided by deep learning, data scarcity remains a challenge in ecology. We explored transfer learning to classify behaviors from acceleration data of critically endangered hawksbill sea turtles (Eretmochelys imbricata). Transfer learning reuses a model trained on one task from a large dataset to solve a related task. We applied this method using a model trained on green turtles (Chelonia mydas) and adapted it to identify hawksbill behaviors like swimming, resting, and feeding. We also compared this to a model trained on human activity data. Results showed an 8% and 4% F1-score improvement with transfer learning from green turtle and human datasets, respectively. Transfer learning allows researchers to adapt existing models to their study species, leveraging deep learning and expanding the use of accelerometers for wildlife monitoring.

Highly durable MXene-based pressure sensor enabled by homogeneous interfacial locking

Pressure sensors with linear high sensitivity and a broad response range enable the capture of subtle pressure changes across various scenarios, making them essential for accurate detection. Conventional methods typically employ porous and layered designs to boost sensitivity but they can compromise stability due to mechanical mismatches and interface incompatibilities between diverse materials. To address these issues, we propose a homogeneous interfacial locking strategy that simultaneously improves the linear response range and stability of the sensor. This approach uses aqueous polyurethane (WPU) as the interface-locking material, which matches the material properties of the substrate polyurethane (PU) sponge. This compatibility anchors the deposited MXene nanosheets to the sponge framework, establishing a robust mechanical and electrical interface. Additionally, the partial filling of the sponge’s pores with WPU enhances its mechanical compressibility. As a result, the sensor achieves high sensitivity (6226 kPa−1), a wide detection range (up to 146 kPa) and excellent durability (over 100,000 cycles), making it suitable for long-term human posture monitoring and analysis. This strategy offers a promising pathway for developing pressure sensors with high sensing performance and improved stability.

Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments

This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers’ postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers’ postures. By conducting experiments across varied manufacturing environments, this research evaluates the system’s effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.

A Field-Programmable Gate Array-Based Adaptive Sleep Posture Analysis Accelerator for Real-Time Monitoring.

This research presents a sleep posture monitoring system designed to assist the elderly and patient attendees. Monitoring sleep posture in real time is challenging, and this approach introduces hardware-based edge computation methods. Initially, we detected the postures using minimally optimized sensing modules and fusion techniques. This was achieved based on subject (human) data at standard and adaptive levels using posture-learning processing elements (PEs). Intermittent posture evaluation was performed with respect to static and adaptive PEs. The final stage was accomplished using the learned subject posture data versus the real-time posture data using posture classification. An FPGA-based Hierarchical Binary Classifier (HBC) algorithm was developed to learn and evaluate sleep posture in real time. The IoT and display devices were used to communicate the monitored posture to attendant/support services. Posture learning and analysis were developed using customized, reconfigurable VLSI architectures for sensor fusion, control, and communication modules in static and adaptive scenarios. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed Board-based field-programmable gate array (FPGA) Xilinx board was used for experimental validation.

Lignin‐Based Macromolecular Photoinitiator for Non‐Migration, Self‐Adhesive, and Water‐Resistant Eutectogels toward Underwater Reliable Communication

AbstractHydrophobic conductive gels have attracted much attention in underwater sensing as the soft robots and wearable sensors. Among the various methods for preparing conductive gels, UV‐initiated polymerization shows advantages of rapid curing and significant energy savings. However, traditional commonly used photoinitiators in UV‐initiated polymerization often migrate or diffuse within conductive gels, especially with prolonged use or underwater, posing potential environmental and health risks. Herein, a novel lignin‐based macromolecular photoinitiator (L‐BF) is first designed by incorporating benzoylformate into the lignin backbone, demonstrating excellent UV absorption, high radical initiation efficiency, and hydrophobicity. Subsequently, hydrophobic eutectogels (HEG) are prepared via UV‐initiated polymerization using L‐BF as photoinitiator. The HEG shows excellent mechanical properties (toughness of 5.67 MJ m−3 and strain of 941%) and scarcely L‐BF migration underwater. Moreover, the hydrophobic network of L‐BF endowed HEG with hydrophobicity (water contact angle exceeds 110°; swelling ratio is <5.8%), superior underwater adhesion (255 kPa), even maintained excellent sensing performance (gauge factor of 4.69), and long‐term reproducibility (20 000 cycles) in air and underwater. Validation confirms the potential of HEG as an effective wearable sensor for underwater posture monitoring and communication, advancing the use of lignin and low‐migration conductive gels in marine exploration.

Clinical Significance of Pose Estimation Methods Compared with Radiographic Parameters in Adolescent Patients with Idiopathic Scoliosis.

Human pose estimation, a computer vision technique that identifies body parts and constructs human body representations from images and videos, has recently demonstrated high performance through deep learning. However, its potential application in clinical photography remains underexplored. This study aimed to establish photographic parameters for patients with adolescent idiopathic scoliosis (AIS) using pose estimation and to determine correlations between these photographic parameters and corresponding radiographic measures. We conducted a study involving 42 patients with AIS who had undergone spinal correction surgery and conservative treatment. Preoperative photographs were captured using an iPhone 13 Pro mounted on a tripod positioned at the head of an X-ray tube. From the outputs of pose estimation, we derived five photographic parameters and subsequently conducted a statistical analysis to assess their correlations with relevant conventional radiographic parameters. In the sagittal plane, we identified significant correlations between photographic and radiographic parameters measuring trunk tilt angles. In the coronal plane, significant correlations were found between photographic parameters measuring shoulder height and trunk tilt and corresponding radiographic measurements. The results suggest that pose estimation, achievable with common mobile devices, offers potential for AIS screening, early detection, and continuous posture monitoring, effectively mitigating the need for X-ray radiation exposure. Level of Evidence: 3.

Research on Athlete Posture Monitoring and Correction Technology Based on Wireless Sensing and Computer Vision Algorithms

Research on Athlete Posture Monitoring and Correction Technology Based on Wireless Sensing and Computer Vision Algorithms

A fully encapsulated flexible triboelectric sensor for swimming posture monitoring

A fully encapsulated flexible triboelectric sensor for swimming posture monitoring

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.

Integrating sEMG and IMU Sensors in an e-Textile Smart Vest for Forward Posture Monitoring: First Steps.

Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut's Elitex fiber outperformed Shieldex's fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm.

Application of IMU Sensors for Real-Time Posture Monitoring and Musculoskeletal Disorder Prevention in Construction Workers

Application of IMU Sensors for Real-Time Posture Monitoring and Musculoskeletal Disorder Prevention in Construction Workers

Flexible silicone “honeycomb” sensing module inspired by the natural irregular holes of Taihu Lake Stone, used for athlete posture monitoring and human-computer interaction

Flexible silicone “honeycomb” sensing module inspired by the natural irregular holes of Taihu Lake Stone, used for athlete posture monitoring and human-computer interaction

Advancing mental health predictions through sleep posture analysis: a stacking ensemble learning approach

Sleep posture is closely related to sleep quality, and can offer insights into an individual’s health. This correlation can potentially aid in the early detection of mental health disorders such as depression and anxiety. Current research focuses on embedding pressure sensors in bedsheets, attaching accelerometers on a subject’s chest, and installing cameras in bedrooms for sleep posture monitoring. However, such solutions sacrifice either the user's sleep comfort or privacy. This study explores the effectiveness of using contactless ultra-wideband (UWB) sensors for sleep posture monitoring. We employed a UWB dataset that is composed of the measurements from 12 volunteers during sleep. A stacking ensemble learning method is introduced for the monitoring of sleep postural transitions, which constitute two levels of learning. At the base-learner level, six transfer learning models (VGG16, ResNet50V2, MobileNet50V2, DenseNet121, VGG19, and ResNet101V2) are trained on the training dataset for initial predictions. Then, the logistic regression is employed as a meta-learner which is trained on the predictions gained from the base-learner to obtain final sleep postural transitions. In addition, a sleep posture monitoring algorithm is presented that can give accurate statistics of total sleep postural transitions. Extensive experiments are conducted, achieving the highest accuracy rate of 86.7% for the classification of sleep postural transitions. Moreover, time-series data augmentation is employed, which improves the accuracy by 13%. The privacy-preserving sleep monitoring solution presented in this paper holds promise for applications in mental health research.

Entertainment robot based on IoT and VR interaction for motion training posture monitoring

With the development of artificial intelligence technology, the role of social robots in sports activities has gradually become prominent. Robots can help with intelligent control of sports venues, assist in motion guidance, and engage in emotional communication with users, helping athletes adjust their emotions. This article studies the motion training posture monitoring of entertainment robots based on the interaction between the Internet of Things and VR. A new high-intensity motion attitude monitoring system based on quantum dot photodetectors has been developed to improve monitoring accuracy and stability. Entertainment robots can simulate and analyze motion training postures, and provide relevant technical guidance. The quantum dot photodetectors were integrated into the sensor components, and the collected data was transmitted to the data acquisition and processing unit through signal transmission and processing modules. The collected data was processed and analyzed to achieve real-time monitoring and tracking of the target object’s attitude. Multiple experiments were conducted on the entire system, simulating various high-intensity exercise environments, and evaluating the performance parameters of the system. Through continuous optimization and improvement, the stability and accuracy of the system were ultimately ensured.

Suppressing charge recombination by synergistic effect of ferromagnetic dual-tribolayer for high output triboelectric nanogenerator

Charge recombination occurring inside the tribolayer limits the further improvement of triboelectric nanogenerator (TENG) output. While the doping of ferromagnetic medium in triboelectric materials has been proven effective in boosting the TENG output, its working mechanism in the tribolayer remains to be investigated. Herein, the dual tribolayer synergistic effect is demonstrated as a means of suppressing surface charge recombination, thus achieving a high peak power density of TENG based on a composite film of polymer/nickel nanoparticles. A dual tribolayer model is proposed to reveal the working mechanism of the ferromagnetic medium for suppressing charge recombination in the positive and negative tribolayer. The charge recombination ratio as a standardized assessment method is defined to quantify the surface charge loss. Owing to the dual tribolayer synergistic effect, the optimal TENG exhibits a record-high peak power density of 15.2 W m-2 Hz-1 with the ultra-low charge recombination ratio of 2.1%, representing a remarkable 3100% increase in comparison to pristine TENG in the atmospheric environment. A self-powered sitting posture monitoring system is developed based on the woven structured DT-TENGs benefiting from the dual-tribolayer synergistic effect. This work demonstrates the superior behavior of TENG in generating ultra-high-power output and provides a significant guidance in materials selection.

V2CTX-MXene-functionalized fluoropolymer composite nanofibrous mat-based high-performance triboelectric nanogenerator for self-powered human activity and posture monitoring

Since triboelectric nanogenerators (TENG) are receiving attention as a promising technology in the domain of self-powered sensors for human activity monitoring and human machine interfaces, significant research is being conducted to enhance their performance and reliability. Herein, a novel V2CTX@poly(vinylidene-fluoride-co-hexafluoropropylene) composite nanofibrous (VPCN) mat was developed as a highly electronegative and stable tribolayer to improve the overall triboelectric performance. An abundant presence of functional groups (-F, -O, and -OH) on the V2CTX surfaces enhanced electronegativity, while formation of micro-capacitor networks into the polymer matrix enhanced the dielectric property along with the electron-trapping capacity, which reduced charge loss. The fabricated VPCN-TENG coupled with polyethylene oxide (PEO) nanofibrous mat exhibited a peak power-density of 18.2 W/m2, which was 3.5-fold higher than the pristine PVDF-HFP nanofibers-based TENG. The remarkable electrical output of the VPCN-TENG facilitated the transformation of biomechanical energy from various human motions into electrical power capable of powering commercial electronics such as stopwatches and thermo-hygrometers. Furthermore, the VPCN-TENG was successfully demonstrated as a self-powered pressure sensor with an extraordinary sensitivity of 25.17 VkPa-1 (1–42 kPa). In addition, the VPCN-TENG was used to fabricate a smart-belt as a self-powered motion sensor for monitoring various human activities such as walking, running, jumping, and posture monitoring during sitting and cycling. These findings offer valuable insights for designing high-performance TENG, fostering development of self-sustainable wearable electronics and advancements in self-powered sensors.

Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review.

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. The prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for the real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart sensing chairs, with a specific focus on the strategies used for posture detection and classification and the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, Google Scholar, Scopus, and PubMed databases yielded 39 pertinent studies that utilized non-invasive methods for posture monitoring. The analysis revealed that Force Sensing Resistors (FSRs) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy due to the constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

Monitoring and Correction of Human Joints Posture using Flex sensor

A large number of the working population have occupations which require long hours of sitting and students spend most of their time on laptop, mobile devices leaning forward, incorrect sleeping posture etc., these mentioned habits lay significant strain on the posture. In fact, after surveying 200 people of various age groups, 76% said that they do not practice good posture. Of those people, more than 90% said that they experience joint pain. A posture monitoring and correction system is developed which is used to enhance the quality of life by providing the support needed to maintain good posture. When the user retains the same posture for a period of 2 minutes, this system will automatically warn the user such that he/she can correct it. The neck posture is monitored by placing the flex sensor over the neck region of C2 – C7 of cervical vertebra and intimated by buzzer sound if the user maintained the same posture for around 120 seconds. This proposed work will be further utilized to analyze the improper use of wrist joint which creates numbness and leads to carpel tunnel syndrome. The flex sensor monitors the wrist joint movement which will placed in the dorsal side of the wrist and give an alarm, besides the maximum extension up to which one can do flexion and extension will also be set so that the over usage of the joint will be intimated by auditory signal.

APC: Contactless healthy sitting posture monitoring with microphone array

The prevalence of poor sitting posture in daily work has become a growing concern among office workers and students due to the associated health problems. To address this issue, we design an acoustic sitting posture care system (termed, APC) based on a circular microphone array. Compared with classic posture recognition technologies such as visual perception and sensors, acoustic sensing naturally possesses advantages such as privacy protection and contactless capabilities. Concretely, our system leverages a customized and inaudible sound signal sent from a speaker to a user’s body, and an echo signal preprocessing method to sense the body posture. Our system comprises three modules: signal generation and collection, signal preprocessing, and posture classification. The signal generation and collection module is designed to create an appropriate signal waveform for transmitting the sound signal. We also develop a unique alignment method for received signals to implement background interference cancellation. In the signal preprocessing module, we propose a body profile extraction method based on the phase difference between received signals. In the posture classification module, we design an attention mechanism based classification network that can map the output of the previous module to different sitting posture categories. The experimental results show that our proposed method achieves an average accuracy of 98.4% for five common sitting postures. Furthermore, case studies conducted under different practical conditions have validated the robustness of our system.