Wearable System Research Articles

Determination of Cortisol Hormone from Sweat Samples and Interpretation with Microcontroller

Cortisol, known as the body's stress hormone, regulates metabolism, inflammation, and the immune system. It affects various bodily systems such as the nervous, immune, cardiovascular, respiratory, reproductive, and musculoskeletal systems. Normal sweat cortisol levels range from 8 to 142 ng/ml, with elevated levels indicating stress (200 ng/ml in women, 500 ng/ml in men). This study focuses on colorimetric cortisol analysis using a microfluidic layer to collect sweat samples. Samples are mixed with a specific color reagent. Cortisol levels are quantitatively analyzed using Z-Score values and RGB channel density via photographs taken with the ESP 32 module. The aim is to determine cortisol levels, aiding in diagnosing conditions like Cushing's syndrome and Addison's Disease, leveraging wearable technology. The developed system integrates a microcontroller into the wearable microfluidic system for cortisol measurements throughout the day. Data is displayed and recorded on a computer for streamlined cortisol monitoring. The blue tetrazolium method used offers stable colors and rapid reactions (10 minutes). The Limit of Detection (LOD) is 0.3 ug/mL, with a detection limit of 1.1 ug/mL. Combined with the ESP 32 Cam module, the system detects cortisol concentrations from 0.8 – 60 ug/mL.

Dual functional bio-inspired additives reconstruct metal-organic interface stability for wearable energy storage system

Aqueous zinc-ion batteries (AZIBs) hold significant promise across wearable energy storage devices due to their superior safety and cost-effectiveness. However, the instability of the electrode/electrolyte interface leads to severe hydrogen evolution reaction (HER) and dendritic growth. Herein, a bio-inspired additive, keratin (Ker), is introduced to construct a robust interfacial layer. The hydrogen bonding donors homogenize the distribution of interfacial hydrogen bond networks and increases the electron density in Zn2+-OH interaction, stabilizing bound H2O molecules and effectively inhibiting HER. Additionally, electron transfer from the highly reactive thiol (-SH) motivates the formation of Zn-S chemical linkage, enhancing interface stability. These features endow Zn||Zn symmetric cells with an impressive cycle life, reaching up to 3364 h at 1 mA cm−2/1 mAh cm−2. The Zn||α-MnO2 and Zn||VO2 full cells also exhibit a substantial capacity retention after 1000 cycles. Incorporating multi-step laser cutting technology enables the fabrication of flexible, large-area micro-batteries with prolonged cycle life. This study presents an advanced methodology for heralding a promising pathway to improve the reversibility of aqueous energy storage systems.

Integrated Approach for Human Wellbeing and Environmental Assessment Based on a Wearable IoT System: A Pilot Case Study in Singapore.

This study presents the results of the practical application of the first prototype of WEMoS, the Wearable Environmental Monitoring System, in a real case study in Singapore, along with two other wearables, a smart wristband to monitor physiological data and a smartwatch with an application (Cozie) used to acquire users' feedback. The main objective of this study is to present a new procedure to assess users' perceptions of the environmental quality by taking into account a multi-domain approach, considering all four environmental domains (thermal, visual, acoustic, and air quality) through a complete wearable system when users are immersed in their familiar environment. This enables an alternative to laboratory tests where the participants are in unfamiliar spaces. We analysed seven-day data in Singapore using a descriptive and predictive approach. We have found that it is possible to use a complete wearable system and apply it in real-world contexts. The WEMoS data, combined with physiology and user feedback, identify the key comfort features. The transition from short-term laboratory analysis to long-term real-world context using wearables enables the prediction of overall comfort perception in a new way that considers all potentially influential factors of the environment in which the user is immersed. This system could help us understand the effects of exposure to different environmental stimuli thus allowing us to consider the complex interaction of multi-domains on the user's perception and find out how various spaces, both indoor and outdoor, can affect our perception of IEQ.

Gesture Recognition Framework for Teleoperation of Infrared (IR) Consumer Devices Using a Novel pFMG Soft Armband.

Wearable technologies represent a significant advancement in facilitating communication between humans and machines. Powered by artificial intelligence (AI), human gestures detected by wearable sensors can provide people with seamless interaction with physical, digital, and mixed environments. In this paper, the foundations of a gesture-recognition framework for the teleoperation of infrared consumer electronics are established. This framework is based on force myography data of the upper forearm, acquired from a prototype novel soft pressure-based force myography (pFMG) armband. Here, the sub-processes of the framework are detailed, including the acquisition of infrared and force myography data; pre-processing; feature construction/selection; classifier selection; post-processing; and interfacing/actuation. The gesture recognition system is evaluated using 12 subjects' force myography data obtained whilst performing five classes of gestures. Our results demonstrate an inter-session and inter-trial gesture average recognition accuracy of approximately 92.2% and 88.9%, respectively. The gesture recognition framework was successfully able to teleoperate several infrared consumer electronics as a wearable, safe and affordable human-machine interface system. The contribution of this study centres around proposing and demonstrating a user-centred design methodology to allow direct human-machine interaction and interface for applications where humans and devices are in the same loop or coexist, as typified between users and infrared-communicating devices in this study.

Effects of Co-doping on the microstructure and electrochemical performance of nickel vanadate (Ni3V2O8) electrode material for aqueous symmetric supercapacitor

Recently, there has been an increased focus on supercapacitors due to their reliable electrochemical energy storage capabilities. This study utilized an environmentally friendly facile hydrothermal method to prepare the nickel vanadate (NiVO) and cobalt-doped nickel vanadate (Co-doped Ni3V2O8) composite for supercapacitor applications. The prepared materials of pure and Co-doped Ni3V2O8 with different concentrations of Co (10 % and 15 %) were evaluated through three electrode assemblies. It is significant to note that although Co smoothed the surface of NiVO, most of the nickel vanadate crystals broke apart into tiny particles and increased the number of electroactive sites. Pure NiVO has a specific capacitance (Cs) of 1638.06 F/g at 5 mV/s. It has been observed that the electrochemical performance has been improved by adding Co 10 % and 15 % in NiVO. The 15 % Co-doped NiVO exhibits an outstanding Cs of 2641.99 F/g at a scan rate of 5 mV/s with a high capacitance retention of 98.14 % after 1k charge-discharges cycles. This excellent performance of the material is due to more electroactive sites and synergistic effects among Co and Ni in the composite. To further elaborate the electrochemical performance of 15 % Co-doped NiVO, it is employed for the fabrication of a symmetries supercapacitor (SSC) device, which shows that the fabricated device has a capacitance of 286 F/g at a current density (CD) of 0.5 A/g and high energy density of 39.7 Wh/kg at a power density of 333.33 W/kg. The symmetric device maintains a cyclic stability of 93.8 % while undergoing 5000 consecutive charge–discharge cycles at a current density of 10 A/g. Based on rigorous testing and analysis, these findings suggest that the prepared electrode material is reliable and suitable for implementation in forthcoming wearable electronic systems.

A wearable adaptive penile rigidity monitoring system for assessment of erectile dysfunction

Erectile dysfunction (ED) is a prevalent type of sexual dysfunction, and continuous monitoring of penile tumescence and rigidity during spontaneous nocturnal erections is crucial for its diagnosis and classification. However, the current clinical standard device, limited by its active mechanical load, is bulky and nonwearable and strongly interferes with erections, which compromises both monitoring reliability and patient compliance. Here, we report a wearable adaptive rigidity monitoring (WARM) system that employs a measurement principle without active loads, allowing for the assessment of penile tumescence and rigidity through a specifically designed elastic dual-ring sensor. The dual-ring sensor, comprising two strain-sensing rings with distinct elastic moduli, provides high resolution (0.1%), robust mechanical and electrical stability (sustaining over 1000 cycles), and strong interference resistance. An integrated flexible printed circuit (FPC) collects and processes sensing signals, which are then transmitted to the host computer via Bluetooth for ED assessment. Additionally, we validated the WARM system against the clinical standard device using both a penile model and healthy volunteers, achieving high consistency. Furthermore, the system facilitates the continuous evaluation of penile erections during nocturnal tumescence tests with concurrent sleep monitoring, demonstrating its ability to minimize interference with nocturnal erections. In conclusion, the WARM system offers a fully integrated, wearable solution for continuous, precise, and patient-friendly measurement of penile tumescence and rigidity, potentially providing more reliable and accessible outcomes than existing technologies.Erectile dysfunction (ED) is a prevalent sexual dysfunction, and continuous monitoring of penile tumescence and rigidity during spontaneous nocturnal erections is crucial for its diagnosis and classification. However, the current clinical standard device, limited by its active mechanical load, is bulky, nonwearable, and creates pronounced interference with erections, which compromises both monitoring reliability and patient compliance. Here, we report a wearable adaptive rigidity monitoring (WARM) system (Fig. 1a) that employs a measurement principle without active loads (Fig. 1b), allowing for the assessment of penile tumescence and rigidity through a specifically designed elastic dual-ring sensor. The dual-ring sensor, comprising two strain-sensing rings with distinct elastic moduli, provides high resolution (0.1%), robust mechanical and electrical stability (sustaining over 1000 cycles), and strong interference resistance. Additionally, we validate the WARM system against the clinical standard device using both a penile model and healthy volunteers, achieving high consistency. Furthermore, the system facilitates the continuous evaluation of penile erections during nocturnal tumescence tests, with concurrent sleep monitoring, demonstrating its ability to minimize interference with nocturnal erections (Fig. 1c). In conclusion, the WARM system offers a fully integrated, wearable solution for continuous, precise, and patient-friendly measurement of penile tumescence and rigidity, potentially providing more reliable and accessible outcomes than those from existing technologies.

Gold Nanowire Sponge Electrochemistry for Permeable Wearable Sweat Analysis Comfortably and Wirelessly.

Electrochemistry-based wearable and wireless sweat analysis is emerging as a promising noninvasive method for real-time health monitoring by tracking chemical and biological markers without the need for invasive blood sampling. It offers the potential to remotely monitor human sweat conditions in relation to metabolic health, stress, and electrolyte balance, which have implications for athletes, patients with chronic conditions, and individuals for the early detection and management of health issues. The state-of-the-art mainstream technology is dominated by the concept of a wearable microfluidic chip, typically based on elastomeric PDMS. While outstanding sensing performance can be realized, the design suffers from the poor permeability of PDMS, which could cause skin redness or irritation. Here, we introduce an omnidirectionally permeable, deformable, and wearable sweat analysis system based on gold nanowire sponges. We demonstrate the concept of all-in-one soft sponge electrochemistry, where the working, reference, and counter electrodes and electrolytes are all integrated within the sponge matrix. The intrinsic porosity of sponge in conjunction with vertically aligned gold nanowire electrodes gives rise to a high electrochemically active surface area of ∼67 cm2. Remarkably, this all-in-one sponge-based electrochemical system exhibited stable performance under a pressure of 10 kPa and 300% omnidirectional strain. The gold sponge biosensing electrodes could be sandwiched between two biocompatible sweat pads, which can serve as natural sweat collection and outflow layers. This naturally biocompatible and permeable platform can be integrated with wireless communication circuits, leading to a wireless sweat analysis system for the real-time monitoring of glucose, lactate, and pH during exercise.

An Actively Vision-Assisted Low-Load Wearable Hand Function Mirror Rehabilitation System

The restoration of fine motor function in the hand is crucial for stroke survivors with hemiplegia to reintegrate into daily life and presents a significant challenge in post-stroke rehabilitation. Current mirror rehabilitation systems based on wearable devices require medical professionals or caregivers to assist patients in donning sensor gloves on the healthy side, thus hindering autonomous training, increasing labor costs, and imposing psychological burdens on patients. This study developed a low-load wearable hand function mirror rehabilitation robotic system based on visual gesture recognition. The system incorporates an active visual apparatus capable of adjusting its position and viewpoint autonomously, enabling the subtle monitoring of the healthy side’s gesture throughout the rehabilitation process. Consequently, patients only need to wear the device on their impaired hand to complete the mirror training, facilitating independent rehabilitation exercises. An algorithm based on hand key point gesture recognition was developed, which is capable of automatically identifying eight distinct gestures. Additionally, the system supports remote audio–video interaction during training sessions, addressing the lack of professional guidance in independent rehabilitation. A prototype of the system was constructed, a dataset for hand gesture recognition was collected, and the system’s performance as well as functionality were rigorously tested. The results indicate that the gesture recognition accuracy exceeds 90% under ten-fold cross-validation. The system enables operators to independently complete hand rehabilitation training, while the active visual system accommodates a patient’s rehabilitation needs across different postures. This study explores methods for autonomous hand function rehabilitation training, thereby offering valuable insights for future research on hand function recovery.

Pressure Visualization and Quantification Photonic Skin Based on Flexible Optical Fiber Combiner

AbstractThe integration of pressure quantification and visualization enhances pressure perception, accuracy, and efficiency. Current solutions require improvements in quantization accuracy, structural simplicity, and integration. We proposed a novel photonic skin comprising a 3 × 1 flexible optical fiber combiner, integrating red, green, and blue light emitting diode (LED) chips through three flexible optical fibers. Under pressure, changes in the optical fibers' transmission loss alter the output light intensity ratio, thus inducing a color shift at the combiner's output. This visible change can be precisely quantified using a color sensor chip. Performance metrics include sensing range up to 33N, sensitivity between 0.04 and 0.24 dB N−1, detection limit below 0.08 N, response time of 500 ms, recovery time of 400 ms, and durability exceeding 2000 cycles. A compact flexible circuit board manages light source driving, data acquisition, and wireless communication, forming a wearable photonic skin system. This system enables visual recognition and quantitative measurement of pressure across diverse scenarios, including different tactile modes, multi‐position pressure, finger bending, and neck movement. For oral occlusion force detection, the spatial separation of the sensing and visualization areas enables the system to simultaneously provide accurate measurements and intuitive visual assessment.

Auricular laser acupuncture as an adjunct for parental anxietymanagement during children's surgery: Arandomized-controlled study.

Pediatric surgery is associated with high levels of anxiety for both children and parents/caregivers. To mitigate anxiety, auricular acupuncture has shown its potential in the perioperative setting. Accordingly, our team developed a wearable prototype auricular laser acupuncture system, AcuHealth V1.0, as a portable acupuncture device and conducted a proof-of-concept evaluation with parents of children undergoing surgery. The primary aim of this study was to conduct feasibility testing of the AcuHealth V1.0 system in delivering auricular laser acupuncture. Parents of children who were scheduled to undergo outpatient surgery were randomly assigned to one of three groups: authentic acupuncture (laser beams at known anxiolytic acupoints, n = 13), sham acupuncture (non-anxiolytic acupoints, n = 14), or a placebo control group (inactive laser, n = 14). Parent self-reported anxiety (0-10 numerical rating scale) was assessed at baseline, pre-intervention (once child was taken to the OR), post-intervention, and at 30 min after the intervention. Usability and acceptability data regarding the device were assessed after the intervention. Baseline data revealed no significant difference in anxiety between the three groups. Parent-reported anxiety level at 30-min post-intervention as compared to baseline in the authentic group was significantly decreased (delta mean ± std = -3.58 ± 2.07) compared to both the sham acupuncture (-1.35 ± 2.65) and placebo control group (0.54 ± 1.13). Evaluation of changes in parent-reported anxiety between groups over time using two-way repeated-measures analysis of variance (ANOVA) revealed a significant difference between the three groups (p = 0.001). Post hoc analysis with Scheffe test pairwise comparisons showed that at 30-min post-intervention compared to baseline, the authentic intervention group was significantly less anxious compared with both the sham group (p = 0.033) and the placebo control group (p = 0.001). Additionally, feedback regarding the usage of the device supported the acceptability and usability of the device with no adverse events. This pilot study administering laser auricular acupuncture via the AcuHealth V1.0 system decreased parental anxiety after 30 min in parents who received treatment immediately after their children were taken to the operating room with no adverse effect.

Coaxial direct writing of ultra-strong supercapacitors with braided continuous carbon fiber based electrodes

Supercapacitors produced based on 3D printing technology greatly expand the range of materials and geometric complexity. However, the production of fiber-based supercapacitors typicallyrequires subsequent steps. For the first time, we report a coaxial direct writing technique that enables the one-step fabrication of braided flexible solid-state supercapacitors. Continuous carbon fiber is used as a flexible substrate. α-manganese dioxide nanowires and activated carbon are used as active materials. The entire electrode assembly is coated with solid-state electrolyte and then braided. Through coaxial direct writing technology, the braided electrodes and sealings can be extruded by a coaxial nozzle to continuously print free-form ultra-strong supercapacitors with a tensile strength of the braided electrodes up to 636 MPa. Electrochemical measurement results show that the printed capacitor exhibits an excellent specific capacitance, and it still maintains a capacitance retention of 90.1% after 1000 cycles. The supercapacitor exhibits nearly identical electrochemical capacitive characteristics at various bent angles. This work paves the way for integrating enhanced durability and adaptability into next-generation wearable and portable electronic systems.

Wide-response-range and high-sensitivity piezoresistive sensors with triple periodic minimal surface (TPMS) structures for wearable human-computer interaction systems

High-performance pressure sensors are garnering interest in human-computer interaction technology, wearable devices, and bionic electronic skin development. However, highly sensitive sensors frequently have a limited response range. In this work, we developed composites with outstanding conductive network structures through the synergistic effect of transition metal carbides (MXene) and multi-walled carbon nanotubes (MWCNTs). Additionally, pressure sensors with various TPMS structures were prepared using innovative parametric design and Fused Deposition Molding (FDM) printing. Due to the stable synergistic conductive network and distinctive curved surface structure, the sensors exhibit exceptional sensing performance. This includes high sensitivity ranging from 4.67 MPa−1 to 7.03 MPa−1 (within the range of 0–0.1 MPa), a broad operating range (maximum 10 MPa), rapid response and recovery times (326 ms/193.4 ms), and long-term fatigue resistance (over 10,000 s cycles). By integrating mechanical properties, sensing properties, and finite element simulations, we analyzed the mechanism underlying the impact of various TPMS pore structures on the sensitivity and response range of the pressure sensor. In addition, the sensors were arrayed as 4 × 4 modules to successfully recognize a wide range of foot movements from different volunteers. These findings illuminate potential applications in human motion detection, healthcare rehabilitation, and artificial intelligence.

Advancing Healthcare Monitoring: Integrating Machine Learning With Innovative Wearable and Wireless Systems for Comprehensive Patient Care

Advancing Healthcare Monitoring: Integrating Machine Learning With Innovative Wearable and Wireless Systems for Comprehensive Patient Care

Self-powered wearable remote control system based on self-adhesive, self-healing, and tough hydrogels

The advent of the fifth-generation mobile communication network era induces a great potential for the design and development of self-powered wearable electronic devices. Hydrogels, as a typical material for flexible electrodes, have been widely employed in self-powered wearable electronic devices. However, the hydrogel-based triboelectric nanogenerator (H-TENG) is usually compromised by the challenges in data collection, operational stability, and manufacturability due to the unstable combination between the friction layer and the electrode layer, the occurrence of hydrogel damage during prolonged operation, and also the difficulty of machining sticky hydrogel electrodes. In the present study, a composite hydrogel composed of acrylamide, crotonyl alcohol, and 4-carboxyphenylboronic acid was synthesized via ultraviolet crosslinking, which was specially developed for H-TENG and wearable wireless remote control interface, featuring with excellent stretchability and mechanical strength as well as self-healing and self-adhesive properties. The open-circuit voltage of the H-TENG prepared with the hydrogel reached up to 165 V, and the output voltage remained almost unchanged following 10,000 cycles of compression test. Additionally, a wearable wireless remote control system was designed that could accurately control the multiple appliances and adjust different working modes using the H-TENG. Consequently, the development of the H-TENG and wearable wireless remote control system has the potential to provide a new methodology for the application of next-generation wearable devices in various areas, such as smart homes, as a representative example.

Cathode‐Free Aqueous Micro‐battery for an All‐in‐One Wearable System with Ultralong Stability

Cathode‐Free Aqueous Micro‐battery for an All‐in‐One Wearable System with Ultralong Stability

Automatic motion artifact detection in electrodermal activity signals using 1D U-net architecture

We developed a method for automated detection of motion and noise artifacts (MNA) in electrodermal activity (EDA) signals, based on a one-dimensional U-Net architecture. EDA has been widely employed in diverse applications to assess sympathetic functions. However, EDA signals can be easily corrupted by MNA, which frequently occur in wearable systems, particularly those used for ambulatory recording. MNA can lead to false decisions, resulting in inaccurate assessment and diagnosis. Several approaches have been proposed for MNA detection; however, questions remain regarding the generalizability and the feasibility of implementation of the algorithms in real-time especially those involving deep learning approaches. In this work, we propose a deep learning approach based on a one-dimensional U-Net architecture using spectrograms of EDA for MNA detection. We developed our method using four distinct datasets, including two independent testing datasets, with a total of 9602 128-s EDA segments from 104 subjects. Our proposed scheme, including data augmentation, spectrogram computation, and 1D U-Net, yielded balanced accuracies of 80.0 ± 13.7 % and 75.0 ± 14.0 % for the two independent test datasets; these results are better than or comparable to those of other five state-of-the-art methods. Additionally, the computation time of our feature computation and machine learning classification was significantly lower than that of other methods (p < .001). The model requires only 0.28 MB of memory, which is far smaller than the two deep learning approaches (4.93 and 54.59 MB) which were used as comparisons to our study. Our model can be implemented in real-time in embedded systems, even with limited memory and an inefficient microprocessor, without compromising the accuracy of MNA detection.

Remotely Powered Two-Wire Cooperative Sensors for Bioimpedance Imaging Wearables.

Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biological tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross section of the chest or a limb), bioimpedance imaging is called electrical impedance tomography (EIT) and results in functional 2D images. Conventional EIT systems rely on individually cabling each electrode to master electronics in a star configuration. This approach works well for rack-mounted equipment; however, the bulkiness of the cabling is unsuitable for a wearable system. Previously presented cooperative sensors solve this cabling problem using active (dry) electrodes connected via a two-wire parallel bus. The bus can be implemented with two unshielded wires or even two conductive textile layers, thus replacing the cumbersome wiring of the conventional star arrangement. Prior research demonstrated cooperative sensors for measuring bioimpedances, successfully realizing a measurement reference signal, sensor synchronization, and data transfer though still relying on individual batteries to power the sensors. Subsequent research using cooperative sensors for biopotential measurements proposed a method to remove batteries from the sensors and have the central unit supply power over the two-wire bus. Building from our previous research, this paper presents the application of this method to the measurement of bioimpedances. Two different approaches are discussed, one using discrete, commercially available components, and the other with an application-specific integrated circuit (ASIC). The initial experimental results reveal that both approaches are feasible, but the ASIC approach offers advantages for medical safety, as well as lower power consumption and a smaller size.

Soft Multimodal Sensors with Decoupled Multimodality and Minimal Wiring for Wearable Systems

AbstractSoft multimodal sensors represent a promising approach for applications in wearable systems and skin‐adhesive devices, benefiting from their flexibility and body compatibility. However, the complexity of sensor wiring design poses challenges while employing multimodal decoupling. Although recent studies have applied various methods, such as adjusting the electrical properties of materials, these methods encounter limitations in signal decoupling when multichannel measurements are implemented. In this study, a signal measurement method utilizing frequency response analysis is developed using a multi‐band band‐stop filter (MBBSF) for multiple multimodal sensors. This approach enables the decoupling of different types of sensors and allows for the simultaneous collection of sensor data through a two‐wire connection. Consequently, it facilitates the concurrent and independent real‐time assessment of strain and force in each sensor. A sensor glove is developed incorporating multiple multimodal sensors with a two‐wire connection to measure grip posture and force. This glove is used effectively to classify unknown objects, thereby demonstrating its practical utility. This practical application provides a promising solution to the complex wiring challenges commonly encountered in multi‐modal sensor wearable systems.

Subject-Independent Wearable P300 Brain-Computer Interface Based on Convolutional Neural Network and Metric Learning.

The calibration procedure for a wearable P300 brain-computer interface (BCI) greatly impact the user experience of the system. Each user needs to spend additional time establishing a decoder adapted to their own brainwaves. Therefore, achieving subject independent is an urgent issue for wearable P300 BCI needs to be addressed. A dataset of electroencephalogram (EEG) signals was constructed from 100 individuals by conducting a P300 speller task with a wearable EEG amplifier. A framework is proposed that initially improves cross-subject consistency of EEG features through a common feature extractor. Subsequently, a simple and compact convolutional neural network (CNN) architecture is employed to learn an embedding sub-space, where the mapped EEG features are maximally separated, while pursuing the minimum distance within the same class and the maximum distance between different classes. Finally, the model's generalization capability was further optimized through fine-tuning. The proposed method significantly boosts the average accuracy of wearable P300 BCI to 73.23 ±7.62% without calibration and 78.75±6.37% with fine-tuning. The results demonstrate the feasibility and excellent performance of our dataset and framework. A calibration-free wearable P300 BCI system is feasible, suggesting significant potential for practical applications of the wearable P300 BCI system.

Unveiling human biomechanics: insights into lower limb responses to disturbances that can trigger a fall.

Slip-related falls are a significant concern, particularly for vulnerable populations such as the elderly and individuals with gait disorders, necessitating effective preventive measures. This manuscript presents a biomechanical study of how the lower limbs react to perturbations that can trigger a slip-like fall, with the ultimate goal of identifying target specifications for developing a wearable robotic system for slip-like fall prevention. Our analysis provides a comprehensive understanding of the natural human biomechanical response to slip perturbations in both slipping and trailing legs, by innovatively collecting parameters from both the sagittal and frontal plane since both play pivotal roles in maintaining stability and preventing falls and thus provide new insights to fall prevention. We investigated various external factors, including gait speed, surface inclination, slipping foot, and perturbation intensity, while collecting diverse data sets encompassing kinematic, spatiotemporal parameters, electromyographic data, as well as torque, range of motion, rotations per minute, detection, and actuation times. The biomechanical response to slip-like perturbations by the hips, knees, and ankles of the slipping leg was characterized by extension, flexion, and plantarflexion moments, respectively. In the trailing leg, responses included hip flexion, knee extension, and ankle plantarflexion. Additionally, these responses were influenced by gait speed, surface inclination, and perturbation intensity. Our study identified target range of motion parameters of 85.19°, 106.34°, and 95.23° for the hips, knees, and ankles, respectively. Furthermore, rotations per minute values ranged from 17.85 to 51.10 for the hip, 21.73 to 63.80 for the knee, and 17.52 to 57.14 for the ankle joints. Finally, flexion/extension torque values were estimated as -3.05 to 3.22 Nm/kg for the hip, -1.70 to 2.34 Nm/kg for the knee, and -2.21 to 0.90 Nm/kg for the ankle joints. This study contributes valuable insights into the biomechanical aspects of slip-like fall prevention and informs the development of wearable robotic systems to enhance safety in vulnerable populations.