One-Channel Wearable Mental Stress State Monitoring System.

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system's capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.

Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings.

Significance: Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge. Aim: The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions. Approach: The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking. Results: Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and 7.3 pW / Hz at 735 and 850 nm, respectively, with a dynamic range of 88 dB. The 3D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. Conclusions: To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( 18 g / module ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring.

Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R2 of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications.

Design and development of an intelligent wearing system for adolescent spinal orthotics.

Adolescent idiopathic scoliosis is the deformity of the human spine in three-dimensional space. It is a disease with a long course and difficult to recover in a short time. Currently, spinal orthotic braces are an effective non-surgical treatment for this condition. However, existing spinal orthotic braces are still deficient. For example, existing spinal orthotics braces have no way of knowing how correct, effective, and comfortable a patient is wearing the orthotics. Based on distributed pressure monitoring technology, this paper conducts pressure acquisition and compensation research on spinal orthotics used by adolescent patients. After analyzing the principles of orthopedics and selecting monitoring points, this paper selects appropriate sensors and calibrates them. An intelligent wearable system for adolescent spinal orthotics was developed based on these findings. The experimental results show that the system can effectively monitor the process of patients wearing spinal orthotic braces. In addition, the system can compensate and visualize the pressure in real-time, so that doctors and patients can know the process and make judgments and adjustments.

Advancements in optical fiber-based wearable sensors for smart health monitoring.

Healthcare system is undergoing a significant transformation from a traditional hospital-centered to an individual-centered one, as a result of escalating chronic diseases, ageing populations, and ever-increasing healthcare costs,. Wearable sensors have become widely used in health monitoring systems since the COVID-19 pandemic. They enable continuous measurement of important health indicators like body temperature, wrist pulse, respiration rate, and non-invasive bio fluids like saliva and perspiration. Over the last few decades, the development has mostly concentrated on electrochemical and electrical wearable sensors. However, due to the drawbacks of such sensors, such as electronic waste, electromagnetic interference, non-electrical security, and poor performance, researchers are exhibiting a strong interest in optical principle-based systems. Fiber-based optical wearables are among the most promising healthcare systems because of advancements in high-sensitivity, durable, multiplexed sensing, and simple integration with flexible materials to improve wearability and simplicity. We present an overview of recent developments in optical fiber-based wearable sensors, focusing on two mechanisms: wavelength interrogation and intensity modulation for the detection of body temperature, pulse rate, respiration rate, body movements, and biomedical noninvasive fluids, with a thorough examination of their benefits and drawbacks. This review also focuses on improving working performance and application techniques for healthcare systems, including the integration of nanomaterials and the usage of the Internet of Things (IoT) with signal processing. Finally, the review concludes with a discussion of the future possibilities and problems for optical fiber-based wearables.

Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings.

Significance: Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic condition remains a significant challenge. Aim: The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide real-time probe 3-D shape estimation to improve the use of fNIRS in everyday conditions. Approach: The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3-D positions in real-time to enable advanced tomographic data analysis and motion tracking. Results: Optical characterization of the MOBI detector reports a noise equivalence power (NEP) of 8.9 and 7.3 pW / H z at 735 nm and 850 nm, respectively, with a dynamic range of 88 dB. The 3-D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared to positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. Conclusions: To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3-D optode position acquisition, combined with lightweight modules (18 g/module) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal.

One of the epileptic patients' challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals. The structure of the proposed CNN for seizure detection and prediction is the same. Considering the requirements of a wearable system, after a few light pre-processing steps, the ECG signal can be used as input to the neural network without any manual feature extraction step. The desired neural network learns purposeful features according to the labelled ECG signals and then performs the classification of these signals. Training of 39-layer CNN for seizure detection and prediction has been done separately. The proposed method can detect seizures with an accuracy of 98.84% and predict them with an accuracy of 94.29%. With this approach, the ECG signal can be a promising indicator for the construction of portable systems for monitoring the status of epileptic patients.

Integrated electronic/fluidic microneedle system for glucose sensing and insulin delivery.

Background: Precise and dynamic blood glucose regulation is paramount for both diagnosing and managing diabetes. Continuous glucose monitoring (CGM) coupled with insulin pumps forms an artificial pancreas, enabling closed-loop control of blood glucose levels. Indeed, this integration necessitates advanced micro-nano fabrication techniques to miniaturize and combine sensing and delivery modules on a single electrode. While microneedle technology can mitigate discomfort, concerns remain regarding infection risk and potential sensitivity limitations due to their short needle length. Methods: This study presents the development of an integrated electronic/fluidic microneedle patch (IEFMN) designed for both glucose sensing and insulin delivery. The use of minimally invasive microneedles mitigates nerve contact and reduces infection risks. The incorporation of wired enzymes addresses the issue of "oxygen deprivation" during glucose detection by decreasing the reliance on oxygen. The glucose-sensing electrodes employ wired enzyme functionalization to achieve lower operating voltages and enhanced resilience to sensor interference. The hollow microneedles' inner channel facilitates precise drug delivery for blood glucose regulation. Results: Our IEFMN-based system demonstrated high sensitivity, selectivity, and a wide response range in glucose detection at relatively low voltages. This effectively reduced interference from both external and internal active substances. The microneedle array ensured painless and minimally invasive skin penetration, while wired enzyme functionalization not only lowered sensing potential but also improved glucose detection accuracy. In vivo, experiments conducted in rats showed that the device could track subcutaneous glucose fluctuations in real-time and deliver insulin to regulate blood glucose levels. Conclusions: Our work suggests that the IEFMN-based system, developed for glucose sensing and insulin delivery, exhibits good performance during in vivo glucose detection and drug delivery. It holds the potential to contribute to real-time, intelligent, and controllable diabetes management.

Reliability, Validity, and Identification Ability of a Commercialized Waist-Attached Inertial Measurement Unit (IMU) Sensor-Based System in Fall Risk Assessment of Older People.

Falls are a prevalent cause of injury among older people. While some wearable inertial measurement unit (IMU) sensor-based systems have been widely investigated for fall risk assessment, their reliability, validity, and identification ability in community-dwelling older people remain unclear. Therefore, this study evaluated the performance of a commercially available IMU sensor-based fall risk assessment system among 20 community-dwelling older recurrent fallers (with a history of ≥2 falls in the past 12 months) and 20 community-dwelling older non-fallers (no history of falls in the past 12 months), together with applying the clinical scale of the Mini-Balance Evaluation Systems Test (Mini-BESTest). The results show that the IMU sensor-based system exhibited a significant moderate to excellent test-retest reliability (ICC = 0.838, p < 0.001), an acceptable level of internal consistency reliability (Spearman's rho = 0.471, p = 0.002), an acceptable convergent validity (Cronbach's α = 0.712), and an area under the curve (AUC) value of 0.590 for the IMU sensor-based receiver-operating characteristic (ROC) curve. The findings suggest that while the evaluated IMU sensor-based system exhibited good reliability and acceptable validity, it might not be able to fully identify the recurrent fallers and non-fallers in a community-dwelling older population. Further system optimization is still needed.

Perspectives in Wearable Systems in the Human-Robot Interaction (HRI) Field.

Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human-machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system's volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development.

Uneven terrain affects metabolic cost and gait in simulated complex lunar surfaces.

Objective. Upcoming missions of the National Aeronautics and Space Administration (NASA) to the Moon will include extensive human exploration of the lunar surface. Walking will be essential for many exploration tasks, and metabolic cost during ambulation on simulated complex lunar surfaces requires further characterization. In this study, ten healthy subjects (6 male and 4 female) participated in three simulated lunar terrain walking conditions at the NASA Johnson Space Center's planetary 'Rock Yard': (1) flat terrain, (2) flat terrain with obstacles, and (3) mixed terrain.Approach.Energy expenditure and gait were quantified with a wearable metabolic energy expenditure monitoring system and body-worn inertial measurement units (IMUs), respectively.Main results.It was found that participants walking on the mixed terrain, representing the highest workload condition, required significantly higher metabolic costs than in other terrain conditions (p< 0.001). Additionally, our novel IMU-based gait variables discriminated different terrains and identified changes in gait in simulated lunar terrain environments.Significance.Our results showed that the various surface irregularities and inconsistencies could cause additional physical effort while walking on the complex terrain. These findings provide insight into the effects of terrain on metabolic energy expenditure during simulated lunar extravehicular activities.

The future of wearable EEG: a review of ear-EEG technology and its applications.

Objective.This review paper provides a comprehensive overview of ear-electroencephalogram (EEG) technology, which involves recording EEG signals from electrodes placed in or around the ear, and its applications in the field of neural engineering.Approach.We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field.Main results.Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring.Significance.This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.

A convenient approach for knee kinematics assessment using wearable inertial sensors during home-based rehabilitation: Validation with an optoelectronic system.

Rehabilitation services are among the most severely impacted by the COVID-19 pandemic. This has increased the number of people not receiving the needed rehabilitation care. Home-based rehabilitation becomes alternative support to face this greater need. However, monitoring kinematics parameters during rehabilitation exercises is critical for an effective recovery. This work proposes a detailed framework to estimate knee kinematics using a wearable Magnetic and Inertial Measurement Unit (MIMU). That allows at-home monitoring for knee rehabilitation progress. Two MIMU sensors were attached to the shank and thigh segments respectively. First, the absolute orientation of each sensor was estimated using a sensor fusion algorithm. Second, these sensor orientations were transformed to segments orientations using a functional sensor-to-segment (STS) alignment. Third, the relative orientation between segments, i.e., knee joint angle, was computed and the relevant kinematics parameters were extracted. Then, the validity of our approach was evaluated with a gold-standard optoelectronic system. Seven participants completed three to five Timed-Up-and-Go (TUG) tests. The estimated knee angle was compared to the reference angle. Root-mean-square error (RMSE), correlation coefficient, and Bland-Altman analysis were considered as evaluation metrics. Our results showed reasonable accuracy (RMSE < 8°), strong to very-strong correlation (correlation coefficient > 0.86), a mean difference within 1.1°, and agreement limits from -16° to 14°. In addition, no significant difference was found (p-value > 0.05) in extracted kinematics parameters between both systems. The proposed approach might represent a suitable alternative for the assessment of knee rehabilitation progress in a home context.

Base of Support, Step Length and Stride Width Estimation during Walking Using an Inertial and Infrared Wearable System.

The analysis of the stability of human gait may be effectively performed when estimates of the base of support are available. The base of support area is defined by the relative position of the feet when they are in contact with the ground and it is closely related to additional parameters such as step length and stride width. These parameters may be determined in the laboratory using either a stereophotogrammetric system or an instrumented mat. Unfortunately, their estimation in the real world is still an unaccomplished goal. This study aims at proposing a novel, compact wearable system, including a magneto-inertial measurement unit and two time-of-flight proximity sensors, suitable for the estimation of the base of support parameters. The wearable system was tested and validated on thirteen healthy adults walking at three self-selected speeds (slow, comfortable, and fast). Results were compared with the concurrent stereophotogrammetric data, used as the gold standard. The root mean square errors for the step length, stride width and base of support area varied from slow to high speed between 10-46 mm, 14-18 mm, and 39-52 cm2, respectively. The mean overlap of the base of support area as obtained with the wearable system and with the stereophotogrammetric system ranged between 70% and 89%. Thus, this study suggested that the proposed wearable solution is a valid tool for the estimation of the base of support parameters out of the laboratory.

A simplified model for whole-body angular momentum calculation.

The capability to monitor gait stability during everyday life could provide key information to guide clinical intervention to patients with lower limb disabilities. Whole body angular momentum (Lbody) is a convenient stability indicator for wearable motion capture systems. However, Lbody is costly to estimate, because it requires monitoring all major body segment using expensive sensor elements. In this study, we developed a simplified rigid body model by merging connected body segments to reduce the number of body segments, which need to be monitored. We demonstrated that the Lbody could be estimated by a seven-segment model accurately for both people with and without lower extremity amputation.

Simultaneous detection of ultraviolet irradiation and vitamin C using an all-carbon-based integrated wearable system powering by a micro-supercapacitor.

Ultraviolet (UV) radiation is a harmful exogenous factor for human skin. Wearable UV photodetectors can monitor UV exposure in the surroundings, and wearable vitamin C (VC) sensors tracking the levels in the human body present the potential ability to defend the UV radiation. Herein, we reported on the fabrication of an all-in-one wearable system with a UV photodetector and VC sensor powered by a micro-supercapacitor. Based on direct laser writing carbonization of polyimide sheets, the patterned electrodes and interconnects of the circuit were fabricated by a facile one-step operation, obtaining an all-carbon-based integrated system. Such a system exhibited outstanding energy storage ability (56.2 µWh cm-2 at 4.17 mW cm-2), high areal capacitance (1.06 mF cm-2 at 5 mV s-1), satisfying capacitive stability, and good mechanical flexibility. The UV photodetector and the VC sensor were powered to obtain a linear range of UV intensity from 11 to 44 µW cm-2 (equivalent to Ultraviolet Index 4.4 to 17.6), and VC levels of 1.0-200 µM with a low limit of detection of 0.83 µM. Furthermore, the integrated system was successfully applied to the determination of VC in commercial beverage and human sweat samples. This work provided a simple and promising method to fabricate integrated wearable systems for on-site providing information on the UV intensity of the external environment and the VC level of the human body simultaneously.

Adaptive Data Transmission Algorithm for the System of Inertial Sensors for Hand Movement Acquisition.

Modern systems of intelligent sensors commonly use radio data transmission. Hand movement acquisition with the use of inertial sensors requires the processing and transmission of a relatively large amount of data, which may be associated with a significant load on the network structure. Network traffic limitation, without losing the quality of monitoring parameters from the sensor system, is therefore important for the functioning of the radio network which integrates both the teletransmission sensor system and the data acquisition server. The paper presents a wearable solution for hand movement acquisition, which uses data transmission in the Wi-Fi standard and contains 16 MEMS (Micro Electro Mechanical System) sensors. An adaptive algorithm to control radio data transmission for the sensor system has been proposed. The algorithm implemented in the embedded system controls the change of the frame length, the length of the transmission frame and the frequency of its sending, which reduces the load on the network router. The use of the algorithm makes it possible to reduce the power consumption by the sensor system by up to 19.9% and to limit the number of data transferred by up to about 91.6%, without losing the quality of the monitored signal. The data analysis showed no statistically significant differences (p > 0.05) between the signal reconstructed from the complete data and processed by the algorithm.

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence.

Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject's motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target's motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework.

Wireless Communication Platform Based on an Embroidered Antenna-Sensor for Real-Time Breathing Detection.

Wearable technology has been getting more attention for monitoring vital signs in various medical fields, particularly in breathing monitoring. To monitor respiratory patterns, there is a current set of challenges related to the lack of user comfort, reliability, and rigidity of the systems, as well as challenges related to processing data. Therefore, the need to develop user-friendly and reliable wireless approaches to address these problems is required. In this paper, a novel, full, compact textile breathing sensor is investigated. Specifically, an embroidered meander dipole antenna sensor integrated into an e-textile T-shirt with a Bluetooth transmitter for real-time breathing monitoring was developed and tested. The proposed antenna-based sensor is designed to transmit data over wireless communication networks at 2.4 GHz and is made of a silver-coated nylon thread. The sensing mechanism of the proposed system is based on the detection of a received signal strength indicator (RSSI) transmitted wirelessly by the antenna-based sensor, which is found to be sensitive to stretch. The respiratory system is placed on the middle of the human chest; the area of the proposed system is 4.5 × 0.48 cm2, with 2.36 × 3.17 cm2 covered by the transmitter module. The respiratory signal is extracted from the variation of the RSSI signal emitted at 2.4 GHz from the detuned embroidered antenna-based sensor embedded into a commercial T-shirt and detected using a laptop. The experimental results demonstrated that breathing signals can be acquired wirelessly by the RSSI via Bluetooth. The RSSI range change was from -80 dBm to -72 dBm, -88 dBm to -79 dBm and -85 dBm to -80 dBm during inspiration and expiration for normal breathing, speaking and movement, respectively. We tested the feasibility assessment for breathing monitoring and we demonstrated experimentally that the standard wireless networks, which measure the RSSI signal via standard Bluetooth protocol, can be used to detect human respiratory status and patterns in real time.

Preliminary Assessment of a Flexible Multi-Sensor Wearable System Based on Fiber Bragg Gratings for Respiratory Monitoring of Hemiplegic Patients.

Respiratory diseases are common in post-stroke hemiplegic patients and represent a major social problem as they worsen the quality of life and reduce the life span. As a consequence, being able to monitor respiratory parameters such as the respiratory rate (RR) and assess the presence of respiratory asynchronies could be of paramount importance to define hemiplegics' health status. Moreover, RR is a useful parameter to investigate the level of fatigue and distress that these patients undergo during rehabilitation processes. Although motion capture systems and flowmeters are the leading instruments for respiratory pattern evaluation, smart wearable systems are gaining ever more acceptance since they allow continuous monitoring by detecting chest wall breathing displacements, ensuring reduced costs and no need for dedicated spaces. Among other sensing technologies, fiber Bragg grating (FBG) sensors have emerged thanks to their high sensitivity to strain, lightness, and multiplexing capability. In this work, a wearable system composed of four flexible dumbbell-shaped sensing modules is proposed for respiratory monitoring in hemiplegic patients. The system is light and easy to wear and can be adapted to any anthropometry thanks to the modular anchoring system. Its feasibility assessment in RR evaluation was performed on seven hemiplegic volunteers in eupnea and tachypnea breathing conditions. In addition, an explorative investigation was conducted to assess the system's ability to detect asynchronies between torso compartments. The good results suggest that this device could be a useful instrument to support clinicians and operators in hemiplegic patients' management.