Wearable System Research Articles

Comparing feature usage in IMU-based gesture control for omnidirectional robot via wearable glove

<span>To improve the intuitiveness of maneuver control on omniwheeled mobile robot, many hand gesture-based robot controls have been developed. The focus of this research is to develop a wearable system for data acquisition from inertial measurement unit (IMU) sensors and compare its features to be used as gesture recognition using the random forest algorithm. With the need of resource constrained device for wearable system based on microcontrollers, we compared the use of Euler and quaternion-based orientation data as input features. As additional comparison, dimension reduction was also carried out using the principal component analysis (PCA) method. Hand gestures are recognized using data obtained by the IMU sensor embedded in the wearable glove. This study compared the accuracy and size of library files embedded in microcontrollers in several feature usage scenarios. The test evaluation results of all scenarios show that the use of all features provides a balance between high accuracy but small file sizes, respectively 99% and 9.2 KB. However, the use of other fewer features, such as by only using 3 Euler data, 4 quaternion data, or by using PCA algorithm (PC=3) can also be used since the accuracy is still above 90%, with a relatively larger file size.</span>

A Wearable Nose Clip Sensor System for Monitoring Vital Body Signs of Apnea Patients

A Wearable Nose Clip Sensor System for Monitoring Vital Body Signs of Apnea Patients

A 3-D-Printed Multiparametric Wearable System Based on Fiber Bragg Gratings for Shoulder Movement and Respiratory Rate Monitoring

A 3-D-Printed Multiparametric Wearable System Based on Fiber Bragg Gratings for Shoulder Movement and Respiratory Rate Monitoring

A wearable echomyography system based on a single transducer

A wearable echomyography system based on a single transducer

Validity and reliability of running gait measurement with the ViMove2 system.

Running biomechanics have traditionally been analysed in laboratory settings, but this may not reflect natural running gait. Wearable technology has the potential to enable precise monitoring of running gait beyond the laboratory. This study aimed to evaluate the analytical validity and intra-session reliability of temporal running gait outcomes measured by the ViMove2 wearable system in healthy adults. Seventy-four healthy adults (43 males, 31 females, aged 18-55 years) wore the inertial device, ViMove2 on the tibia. Participants ran on a treadmill for one minute at various speeds (8, 10, 12, 14km/hr), completed in a standardised shoe (Saucony Guide Runner). Running gait was measured with the ViMove2 wearable and 3D motion capture (Vicon). Temporal running gait outcomes included ground contact time (GCT) and cadence (steps/min). GCT and cadence from the ViMove2 had face validity with expected changes in outcome with different running speeds, but ViMove2 tended to over-estimate GCT, and under-estimate cadence compared to the reference, especially at slower speeds. GCT demonstrated moderate to good agreement to the reference at speeds >10km/hr, but poor agreement at 8km/hr and within female runners. Cadence had moderate to excellent agreement across speeds compared to the reference. GCT and cadence had excellent reliability across speeds, but at 8km/hr GCT had good agreement between trials. Overall, temporal gait outcomes of GCT and cadence can be measured with the ViMove2, but accuracy and reliability are impacted at slow running speeds and within female runners. Future work is needed to clarify sex or speed-dependent corrections to algorithms / outcomes to aid interpretation and application.

Optimization and synthesis on the dynamics performance of the tensioning and relaxing wearable system in a novel knee exoskeleton using co-simulation

Abstract In this paper, a novel tensioning and relaxing wearable system is introduced to improve the wearing comfort and load-bearing capabilities of knee exoskeletons. The research prototype of the novel system, which features a distinctive overrunning clutch drive, is presented. Through co-simulation with ANSYS, MATLAB, and SOLIDWORKS software, a comprehensive multi-objective optimization is performed to enhance the dynamics performance of the prototype. Firstly, the wearing contact stiffness of the prototype and the mechanical parameters of the relevant materials are simulated and fitted based on the principle of functional equivalence. And then, its equivalent nonlinear circumferential stiffness model is obtained. Secondly, to enhance the wearing comfort of the exoskeleton, a novel comprehensive performance evaluation index, termed wearing comfort, is introduced. The index considers multiple factors such as the duration of vibration transition, the acceleration encountered during wear, and the average pressure applied. Finally, through the utilization of this indicator, the system’s dynamics performance is optimized via multi-platform co-simulation, and the simulation results validate the effectiveness of the research method and the proposed wearable comfort index. The theoretical basis for the subsequent research on the effectiveness of prototype weight-bearing is provided.

A Plantar Pressure Detection and Gait Analysis System Based on Flexible Triboelectric Pressure Sensor Array and Deep Learning.

Gait detection is essential for the assessment of human health status and early diagnosis of diseases. The current gait analysis systems are bulky, limited in the scope of use, and cause interference with the movement of the measured person. Hence, it is necessary to develop a wearable gait detection system that is soft, breathable, lightweight, and self-powered. Here, a plantar pressure sensor array and gait analysis system based on a flexible triboelectric pressure sensor (FTPS) array is developed. Soft, breathable, and wearable electrospinning nanofiber film with excellent triboelectric properties is used as the plantar pressure sensor, achieving a high sensitivity of 45.1 mVkPa-1 in the range of 40-200 kPa and 19.4 mVkPa-1 in the range of 200-400 kpa. 32 FTPSs are integrated into an intelligent insole, which has the characteristics of soft, easy production, good air permeability, long-time stability, no external power supply, and etc. Based on the long short-term memory artificial neural network deep learning model, the accuracy of gait judgment can reach 94.23%. This work provides a feasible solution for real-time gait detection, which will have potential applications in human health assessment and early diagnosis of disease.

Wearable Haptics for Orthotropic Actuation Based on Perpendicularly Nested Auxetic SMA Knotting.

Smart wearable tactile systems, designed to deliver different types of touch feedback on human skin, can significantly improve engagement through diverse actuation patterns in virtual or augmented reality environments. Here, a perpendicularly nested auxetic wearable haptic interface is reported for orthotropically decoupled multimodal actuation (WHOA), capable of producing diverse tactile feedback modes with 3D sensory perception. WHOA incorporates shape memory alloy wires that are intricately knotted into an auxetic structure oriented along orthotropic dual axes. Its perpendicularly nested auxetic structure enables orthotropic actuation, allowing independent expansion and contraction along both x and y-axes, as confirmed by force-strain and displacement-time performance tests. Additionally, the perylene coating provides orthogonal electrical isolation to WHOA, allowing for stripe-specific localized actuation and enabling multiple tactile feedback modes. As an orthotropic wearable haptic interface, WHOA distinguishes between x-axis and y-axis directions and ultimately delivers multi-dimensional information regarding movements in 3D space through tactile feedback. As a result, when worn on the foot or arm, WHOA naturally delivers spatiotemporal tactile information to the user, facilitating navigation and teleoperation with 3D sensory perception.

A Highly Sensitive Strain Sensor with Self-Assembled MXene/Multi-Walled Carbon Nanotube Sliding Networks for Gesture Recognition

Flexible electronics is pursuing a new generation of electronic skin and human–computer interaction. However, effectively detecting large dynamic ranges and highly sensitive human movements remains a challenge. In this study, flexible strain sensors with a self-assembled PDMS/MXene/MWCNT structure are fabricated, in which MXene particles are wrapped and bridged by dense MWCNTs, forming complex sliding conductive networks. Therefore, the strain sensor possesses an impressive sensitivity (gauge factor = 646) and 40% response range. Moreover, a fast response time of 280 ms and detection limit of 0.05% are achieved. The high performance enables good prospects in human detection, like human movement and pulse signals for healthcare. It is also applied to wearable smart data gloves, in which the CNN algorithm is utilized to identify 15 gestures, and the final recognition rate is up to 95%. This comprehensive performance strain sensor is designed for a wide array of human body detection applications and wearable intelligent systems.

A Flexible Smart Healthcare Platform Conjugated with Artificial Epidermis Assembled by Three-Dimensionally Conductive MOF Network for Gas and Pressure Sensing

The rising flexible and intelligent electronics greatly facilitate the noninvasive and timely tracking of physiological information in telemedicine healthcare. Meticulously building bionic-sensitive moieties is vital for designing efficient electronic skin with advanced cognitive functionalities to pluralistically capture external stimuli. However, realistic mimesis, both in the skin’s three-dimensional interlocked hierarchical structures and synchronous encoding multistimuli information capacities, remains a challenging yet vital need for simplifying the design of flexible logic circuits. Herein, we construct an artificial epidermal device by in situ growing Cu3(HHTP)2 particles onto the hollow spherical Ti3C2Tx surface, aiming to concurrently emulate the spinous and granular layers of the skin’s epidermis. The bionic Ti3C2Tx@Cu3(HHTP)2 exhibits independent NO2 and pressure response, as well as novel functionalities such as acoustic signature perception and Morse code-encrypted message communication. Ultimately, a wearable alarming system with a mobile application terminal is self-developed by integrating the bimodular senor into flexible printed circuits. This system can assess risk factors related with asthmatic, such as stimulation of external NO2 gas, abnormal expiratory behavior and exertion degrees of fingers, achieving a recognition accuracy of 97.6% as assisted by a machine learning algorithm. Our work provides a feasible routine to develop intelligent multifunctional healthcare equipment for burgeoning transformative telemedicine diagnosis.

Detecting clinical medication errors with AI enabled wearable cameras

Drug-related errors are a leading cause of preventable patient harm in the clinical setting. We present the first wearable camera system to automatically detect potential errors, prior to medication delivery. We demonstrate that using deep learning algorithms, our system can detect and classify drug labels on syringes and vials in drug preparation events recorded in real-world operating rooms. We created a first-of-its-kind large-scale video dataset from head-mounted cameras comprising 4K footage across 13 anesthesiology providers, 2 hospitals and 17 operating rooms over 55 days. The system was evaluated on 418 drug draw events in routine patient care and a controlled environment and achieved 99.6% sensitivity and 98.8% specificity at detecting vial swap errors. These results suggest that our wearable camera system has the potential to provide a secondary check when a medication is selected for a patient, and a chance to intervene before a potential medical error.

Hierarchically‐Interlocked, Three‐Axis soft Iontronic Sensor for Omnidirectional Shear and Normal Forces

AbstractArtificial tactile sensing capable of measuring shear and normal forces is crucial for the diverse human‐machine interactions and dexterous robotic manipulations. However, existing soft multi‐axis force sensors usually suffer from limited detectable directions and complex coupling mechanisms, limiting their applications in realistic wearable and robotic systems. Here, a hierarchically‐interlocked, three‐axis soft iontronic sensor is presented with an asymmetrical electrode pattern that leverages the mortise‐and‐tenon structure and ultracapacitive principle to detect omnidirectional shear and normal forces. The designed sensor facilitates the decoupling process and achieves enhanced sensing performances including high accuracy, fast response ability, and mechanical robustness. Prototypical integration and application of the sensor are demonstrated to perform wearable telecontrol of virtual platforms and assist robot gripper through closed‐loop force feedback. A sensing array is further developed to construct a touch panel and identify handwriting based on the continuously measured directions of applied forces. This work may offer a potentially promising solution for the next‐generation intelligent electronic skins requiring multimodal tactile sensing information.

Basic Analysis for Evaluation of Tennis Volley Skill Using Body Propagated Vibration Sensing

In sports that use tools, it is necessary to master tool manipulation with a high degree of accuracy because proficiency in tool manipulation leads to victory or defeat in a competition. The purpose of this study was to clarify the force transfer of the body, including the racket, from the perspective of vibration analysis to realize training to efficiently improve tennis skills. In this study, we measured the propagating vibration, surface electromyography of finger flexors and extensors, and racket movement under two conditions of strong- and weak-volleying in tennis and aimed to explain the meaning of the measured propagating vibration in terms of muscle control and racket kinematics. Based on the experimental results, it was confirmed that it is feasible to measure the change in stiffness from the racket to the wrist due to the muscle co-contraction owing to the propagating vibration signal intensity. These results indicate the possibility of evaluating tennis shot skill by analyzing the propagating vibrations measured using a wearable vibration measurement system.

Wearable Online Freezing of Gait Detection and Cueing System.

This paper presents a real-time wearable system designed to assist Parkinson's disease patients experiencing freezing of gait episodes. The system utilizes advanced machine learning models, including convolutional and recurrent neural networks, enhanced with past sample data preprocessing to achieve high accuracy, efficiency, and robustness. By continuously monitoring gait patterns, the system provides timely interventions, improving mobility and reducing the impact of freezing episodes. This paper explores the implementation of a CNN+RNN+PS machine learning model on a microcontroller-based device. The device operates at a real-time processing rate of 40 Hz and is deployed in practical settings to provide 'on demand' vibratory stimulation to patients. This paper examines the system's ability to operate with minimal latency, achieving an average detection delay of just 261 milliseconds and a freezing of gait detection accuracy of 95.1%. While patients received on-demand stimulation, the system's effectiveness was assessed by decreasing the average duration of freezing of gait episodes by 45%. These preliminarily results underscore the potential of personalized, real-time feedback systems in enhancing the quality of life and rehabilitation outcomes for patients with movement disorders.

E-waste driven eco-friendly highly sensitive wearable capacitive sensors for physical activity monitoring and sign language recognition

Owing to the continual and rapid advancement in technology, there has been a significant surge in electronic waste (e-waste) in recent decades, resulting in a harmful impact on our environment. Due to the growing demand for battery-powered devices, non-rechargeable batteries are a significant source of e-waste. This study describes fabricating a paper-based graphite strain sensor that is eco-friendly, inexpensive, biodegradable, and sensitive to the applied strain. Readily accessible materials are employed to design and fabricate MIDC strain sensors. The Modified Interdigital Capacitor (MIDC) structure is formed on basic printing paper, and the conductive substance made with the ink of a gel pen and graphite from a drained dry cell is painted on the paper and used as electrode material. The suggested sensor has a high gauge factor value of 1998.578 and fast response and recovery time of 112 ms and 100 ms, respectively. The proposed prototype has also been utilized to inspect fitness exercises and physical activities on the human body, inspiring the potential for its integration into wearable systems for various applications. Reliable detection and differentiation of finger positions are achieved, highlighting the sensor's potential for more advanced sign language applications. It’s a good option to integrate the suggested strain sensor in wearable systems, such as telemedicine systems, electromechanical detection, healthcare, and physical activity monitoring of humans.

Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India).

The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses.

NeRF-based Localization and Meshing with Wearable Laser Scanning System: A Case Study in Underground Environment

Abstract. Due to the subterranean scene’s poor lighting conditions and variability of the environment, real-time localizing and meshing in complex underground scenes present a challenging task, with high-potential applications in mining and tunnel protection. In this work, we propose a method that combines SLAM and NeRF for mesh reconstruction in the underground environment with a wearable device. First, a LiDAR-Inertial odometry is used for pose estimation. The resulting poses and sequential laser frames are synchronized to generate a novel data structure, scan-block, which is crucial for improving efficiency and ensuring local precision. This structure is designed to enhance efficiency and ensure local precision. Finally, the generated scan-blocks are passed to the backend NeRF for real-time mesh reconstruction. The experiments are carried out in a complex underground space. The experimental results proved that this method achieved good results. The demo video can be found at https://youtu.be/_y1vbaW06EM?si= hFnAl12u-ffU933h.

Smart Green Cotton Textiles with Hierarchically Responsive Conductive Network for Personal Healthcare and Thermal Management.

Whereas cotton as an abundant natural cellulose has been widely used for sustainable and skin-friendly textiles and clothes, developing cotton fabrics with smart functions that could respond to various stimuli is still eagerly desired while remaining a great challenge. Herein, smart multiresponsive cotton fabric with hierarchically copper nanowire interwoven MXene conductive networks that are seamlessly assembled along a 3D woven fabric template for efficient personal healthcare and thermal comfort regulation is successfully developed. The robust hierarchically interwoven conductive network was "glued" and protected by organic conductive polymer poly(3,4-ethylenedioxythiophene) along a 3D interconnected fabric template to enhance interfacial adherent and environmental stability. Benefiting from the robust multiresponsive hierarchically interwoven conductive network, smart cotton fabric exhibits real-time response to various external stimuli (light/electrical/heat/temperature/stress), and the details of human activities can be accurately recognized and monitored. Furthermore, the porous structure of 3D smart fabric induced strong capillary force and confinement to phase change materials PEG, which exhibits a wide range of phase transition temperatures for efficient thermal comfort regulation. After further encapsulation with transparent fluorosilicone resin, the smart cotton fabric exhibits excellent self-cleaning performance with water/oil repellent. The smart multiresponsive cotton fabrics hold great promise in next-generation wearable systems for efficient personal healthcare and thermal management.

Frugal engineering-inspired wearable augmented reality goggle system enables fluorescence-guided cancer surgery

Disparities in surgical outcomes often result from subjective decisions dictated by surgical training, experience, and available resources. To improve outcomes, surgeons have adopted advancements in robotics, endoscopy, and intra-operative imaging including fluorescence-guided surgery (FGS), which highlights tumors and anatomy in real-time. However, technical, economic, and logistic challenges hinder widespread adoption of FGS beyond high-resource centers. To overcome these impediments, we combined laser diodes, Raspberry Pi cameras and computers, off-the-shelf optical components, and 3D-printed parts to make a battery-powered, compact, dual white light and NIR imaging system that has comparable performance to existing bulkier, pricier, and wall-powered technologies. We combined these components with off-the-shelf augmented reality (AR) glasses to create a fully-wearable fluorescence imaging AR Raspberry Pi-based goggle system (FAR-Pi) and validated performance in a pre-clinical cancer surgery model. Novel device design ensures distance-independent coalignment between real and augmented views. As an open-source, affordable, and adaptable system, FAR-Pi is poised to democratize access to FGS and improve health outcomes worldwide.

Variable Admittance Control of High Compatibility Exoskeleton Based on Human–Robotic Interaction Force

The wearable exoskeleton system is a typical strongly coupled human–robotic system. Human–robotic is the environment for each other. The two support each other and compete with each other. Achieving high human–robotic compatibility is the most critical technology for wearable systems. Full structural compatibility can improve the intrinsic safety of the exoskeleton, and precise intention understanding and motion control can improve the comfort of the exoskeleton. This paper first designs a physiologically functional bionic lower limb exoskeleton based on the study of bone and joint functional anatomy and analyzes the drive mapping model of the dual closed-loop four-link knee joint. Secondly, an exoskeleton dual closed-loop controller composed of a position inner loop and a force outer loop is designed. The inner loop of the controller adopts the PID control algorithm, and the outer loop adopts the adaptive admittance control algorithm based on human–robot interaction force (HRI). The controller can adaptively adjust the admittance parameters according to the HRI to respond to dynamic changes in the mechanical and physical parameters of the human–robot system, thereby improving control compliance and the wearing comfort of the exoskeleton system. Finally, we built a joint simulation experiment platform based on SolidWorks/Simulink to conduct virtual prototype simulation experiments and recruited volunteers to wear rehabilitation exoskeletons to conduct related control experiments. Experimental results show that the designed physiologically functional bionic exoskeleton and adaptive admittance controller can significantly improve the accuracy of human–robotic joint motion tracking, effectively reducing human–machine interaction forces and improving the comfort and safety of the wearer. This paper proposes a dual-closed loop four-link knee joint exoskeleton and a variable admittance control method based on HRI, which provides a new method for the design and control of exoskeletons with high compatibility.