Search Results (20)

The current study focuses on the manufacturing and characterization of various forms of Ecoflex and their composites to improve the mechanical properties and surface texture, specifically for use in wearable sensors and electronic skin applications. Various types of Ecoflex elastomers were mixed to form blended composite materials, which could be used to tune the mechanical properties. Experimental and simulation methods were conducted to understand the mechanical behavior and material properties of the manufactured samples under large deformation (1200% strain) by various dynamic loading conditions. Further, the surface conditions of specimens were analyzed and evaluated using scanning electron microscopy and contact angle goniometer. The Yeoh model reasonably predicts the viscoelastic and hysteresis behavior of Ecoflex and its composites in accordance with the experimental data for small and large strain. The surface smoothness and moisture-resistant properties of the material surface were enhanced up to a contact angle of 127° (maximum) by adding x = 15 wt% of surface tension diffusers, with a slight compromise in stretchability. This comprehensive investigation and database of Ecoflex–Ecoflex composite can guide and help researchers in selecting and applying the most appropriate Ecoflex/blended solutions for a specific application, while providing insight into the mechanics of materials of blended materials. Full article

Background: Kinematics experts and physical therapists have implemented the use of sensors for 3D motion analysis, both for static and dynamic movements. XClinic movement sensors are advanced devices designed to analyze movement patterns with high precision. The aim of this study was to validate wearable XClinic sensors for range of motion (ROM) in healthy subjects and obtain normative data. Participants were enrolled at the Sapienza University of Rome in 2024. All participants had to be healthy subjects aged between 18 and 65 years. Data on their demographics, employment and physical activity were collected. All the subjects were tested to assess the active ROM of their shoulder, hip, knee and ankle bilaterally. The same movements were tested using a goniometer to investigate validity, and SF-36 was administered. Fifty subjects were enrolled. The mean age was 28.2 (SD 10.8) years. For the left shoulder, construct validity showed statistically significant values for flexion, extension and extra-rotation, while for the right shoulder, construct validity showed statistically significant values for all movements except intra-rotation. The results concerning the right hip showed statistically significant values for flexion, extra-rotation, intra-rotation and adduction. The left hip showed statistically significant values for all movements except extension. Both the right and left knees showed statistically significant values for flexion. Both the right and left ankles showed statistically significant values for all movements. XClinic sensors offer a reliable and valid solution for the precise monitoring of the ROM of the shoulder and lower limb joints, making them an invaluable asset for clinicians and researchers. Full article

The purpose of this review is to provide a comprehensive resource for shoulder proprioception assessment and its integration into clinical decision making as well as targeted rehabilitation protocols. Data for this review were acquired from peer-reviewed articles from computerized online databases, namely PubMed and Medline, published between 1906 and 2021. The development of digital/smart phone goniometers can improve shoulder joint range of motion (ROM) measurements and demonstrate comparable measurement accuracy to the universal standard goniometer. The inclinometer offers a portable and cost-effective method for measuring shoulder joint angles and arcs of motion in the vertical plane. Two types of dynamometers, the computerized isokinetic machine and the handheld hydraulic dynamometer, are reliable tools for objective shoulder rotator cuff strength assessment. Motion analysis systems are highly advanced modalities that create three-dimensional models of motion arcs using a series of cameras and reflective beads, offering unparalleled precision in shoulder proprioception measurement; however, they require time-consuming calibration and skilled operators. Advancements in wearable devices and compact mobile technology such as iPhone applications may make three-dimensional motion analysis more affordable and practical for outpatient settings in the future. The complex interplay between proprioception and shoulder dysfunction is not fully understood; however, shoulder proprioception can likely both contribute to and be caused by shoulder pathology. In patients with rotator cuff tears, glenohumeral osteoarthritis, and shoulder instability, clinicians can track proprioception to understand a patient’s disease progression or response to treatment. Finally, rehabilitation programs targeting shoulder proprioception have shown promising initial results in restoring function and returning athletes to play. Full article

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual’s body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5–3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9–13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67–82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait. Full article

Human activity recognition (HAR) is essential for the development of robots to assist humans in daily activities. HAR is required to be accurate, fast and suitable for low-cost wearable devices to ensure portable and safe assistance. Current computational methods can achieve accurate recognition results but tend to be computationally expensive, making them unsuitable for the development of wearable robots in terms of speed and processing power. This paper proposes a light-weight architecture for recognition of activities using five inertial measurement units and four goniometers attached to the lower limb. First, a systematic extraction of time-domain features from wearable sensor data is performed. Second, a small high-speed artificial neural network and line search method for cost function optimization are used for activity recognition. The proposed method is systematically validated using a large dataset composed of wearable sensor data from seven activities (sitting, standing, walking, stair ascent/descent, ramp ascent/descent) associated with eight healthy subjects. The accuracy and speed results are compared against methods commonly used for activity recognition including deep neural networks, convolutional neural networks, long short-term memory and convolutional–long short-term memory hybrid networks. The experiments demonstrate that the light-weight architecture can achieve a high recognition accuracy of 98.60%, 93.10% and 84.77% for seen data from seen subjects, unseen data from seen subjects and unseen data from unseen subjects, respectively, and an inference time of 85 $\mathsf{\mu }$s. The results show that the proposed approach can perform accurate and fast activity recognition with a reduced computational complexity suitable for the development of portable assistive devices. Full article

Knee kinematics during gait is an important assessment tool in health-promotion and clinical fields. This study aimed to determine the validity and reliability of a wearable goniometer sensor for measuring knee flexion angles throughout the gait cycle. Twenty-two and seventeen participants were enrolled in the validation and reliability study, respectively. The knee flexion angle during gait was assessed using a wearable goniometer sensor and a standard optical motion analysis system. The coefficient of multiple correlation (CMC) between the two measurement systems was 0.992 ± 0.008. Absolute error (AE) was 3.3 ± 1.5° (range: 1.3–6.2°) for the entire gait cycle. An acceptable AE (<5°) was observed during 0–65% and 87–100% of the gait cycle. Discrete analysis revealed a significant correlation between the two systems (R = 0.608–0.904, p ≤ 0.001). The CMC between the two measurement days with a 1-week interval was 0.988 ± 0.024, and the AE was 2.5 ± 1.2° (range: 1.1–4.5°). A good-to-acceptable AE (<5°) was observed throughout the gait cycle. These results indicate that the wearable goniometer sensor is useful for assessing knee flexion angle during the stance phase of the gait cycle. Full article

The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical–SVM–RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56–81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction. Full article

Wearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing (standing, sitting, or walking). Detection of the activity is done through an algorithm that uses the knee angle and angular speeds as inputs. The measurements of the wearable are compared with a commercial goniometer, and, with the Aktos-t system, a commercial motion capture system based on inertial sensors, at three speeds of gait (4.0 km/h, 4.5 km/h, and 5.0 km/h) in nine participants. Specifically, the four differences between maximum and minimum peaks in the gait cycle, starting with heel-strike, were compared by using the mean absolute error, which was between 2.46 and 12.49 on average. In addition, the algorithm was able to predict the three activities during online testing in one participant and detected on average 94.66% of the gait cycles performed by the participants during offline testing. Full article

Wrist motion provides an important metric for disease monitoring and occupational risk assessment. The collection of wrist kinematics in occupational or other real-world environments could augment traditional observational or video-analysis based assessment. We have developed a low-cost 3D printed wearable device, capable of being produced on consumer grade desktop 3D printers. Here we present a preliminary validation of the device against a gold standard optical motion capture system. Data were collected from 10 participants performing a static angle matching task while seated at a desk. The wearable device output was significantly correlated with the optical motion capture system yielding a coefficient of determination (R${}^2$) of 0.991 and 0.972 for flexion/extension (FE) and radial/ulnar deviation (RUD) respectively (p < 0.0001). Error was similarly low with a root mean squared error of 4.9° (FE) and 3.9° (RUD). Agreement between the two systems was quantified using Bland–Altman analysis, with bias and 95% limits of agreement of 3.1° ± 7.4° and −0.16° ± 7.7° for FE and RUD, respectively. These results compare favourably with current methods for occupational assessment, suggesting strong potential for field implementation. Full article

Wearable sensors have the potential to facilitate remote monitoring for patients recovering from knee replacement surgery. Using IMU sensors attached to the patients’ leg, knee flexion can be monitored while the patients are recovering in their home environment. Ideally, these flexion angle measurements will have an accuracy and repeatability at least on par with current clinical standards. To validate the clinical accuracy of a two-sensor IMU system, knee flexion angles were measured in eight subjects post-TKA and compared with other in-clinic angle measurement techniques. These sensors are aligned to the patients’ anatomy by taking a pose resting their operated leg on a box; an initial goniometer measurement defines the patients’ knee flexion while taking that pose. The repeatability and accuracy of the system was subsequently evaluated by comparing knee flexion angles against goniometer readings and markerless optical motion capture data. The alignment pose was repeatable with a mean absolute error of 1.6 degrees. The sensor accuracy through the range of motion had a mean absolute error of 2.6 degrees. In conclusion, the presented sensor system facilitates a repeatable and accurate measurement of the knee flexion, holding the potential for effective remote monitoring of patients recovering from knee replacement surgery. Full article

Residual motion of upper limbs in individuals who experienced cervical spinal cord injury (CSCI) is vital to achieve functional independence. Several interventions were developed to restore shoulder range of motion (ROM) in CSCI patients. However, shoulder ROM assessment in clinical practice is commonly limited to use of a simple goniometer. Conventional goniometric measurements are operator-dependent and require significant time and effort. Therefore, innovative technology for supporting medical personnel in objectively and reliably measuring the efficacy of treatments for shoulder ROM in CSCI patients would be extremely desirable. This study evaluated the validity of a customized wireless wearable sensors (Inertial Measurement Units—IMUs) system for shoulder ROM assessment in CSCI patients in clinical setting. Eight CSCI patients and eight healthy controls performed four shoulder movements (forward flexion, abduction, and internal and external rotation) with dominant arm. Every movement was evaluated with a goniometer by different testers and with the IMU system at the same time. Validity was evaluated by comparing IMUs and goniometer measurements using Intraclass Correlation Coefficient (ICC) and Limits of Agreement (LOA). inter-tester reliability of IMUs and goniometer measurements was also investigated. Preliminary results provide essential information on the accuracy of the proposed wireless wearable sensors system in acquiring objective measurements of the shoulder movements in CSCI patients. Full article

For the controller of wearable lower-limb assistive devices, quantitative understanding of human locomotion serves as the basis for human motion intent recognition and joint-level motion control. Traditionally, the required gait data are obtained in gait research laboratories, utilizing marker-based optical motion capture systems. Despite the high accuracy of measurement, marker-based systems are largely limited to laboratory environments, making it nearly impossible to collect the desired gait data in real-world daily-living scenarios. To address this problem, the authors propose a novel exoskeleton-based gait data collection system, which provides the capability of conducting independent measurement of lower limb movement without the need for stationary instrumentation. The basis of the system is a lightweight exoskeleton with articulated knee and ankle joints. To minimize the interference to a wearer’s natural lower-limb movement, a unique two-degrees-of-freedom joint design is incorporated, integrating a primary degree of freedom for joint motion measurement with a passive degree of freedom to allow natural joint movement and improve the comfort of use. In addition to the joint-embedded goniometers, the exoskeleton also features multiple positions for the mounting of inertia measurement units (IMUs) as well as foot-plate-embedded force sensing resistors to measure the foot plantar pressure. All sensor signals are routed to a microcontroller for data logging and storage. To validate the exoskeleton-provided joint angle measurement, a comparison study on three healthy participants was conducted, which involves locomotion experiments in various modes, including overground walking, treadmill walking, and sit-to-stand and stand-to-sit transitions. Joint angle trajectories measured with an eight-camera motion capture system served as the benchmark for comparison. Experimental results indicate that the exoskeleton-measured joint angle trajectories closely match those obtained through the optical motion capture system in all modes of locomotion (correlation coefficients of 0.97 and 0.96 for knee and ankle measurements, respectively), clearly demonstrating the accuracy and reliability of the proposed gait measurement system. Full article

This paper presents a method to integrate and package an accelerometer within a textile to create an electronic textile (e-textile). The smallest commercially available accelerometer sensor (2 mm × 2 mm × 0.95 mm) is used in the e-textile and is fully integrated within the weave structure of the fabric itself, rendering it invisible to the wearer. The e-textile forms the basis of a wearable woven sleeve which is applied to arm and knee joint bending angle measurement. The integrated e-textile based accelerometer sensor system is used to identify activity type, such as walking or running, and count the total number of steps taken. Performance was verified by comparing measurements of specific elbow joint angles over the range of 0° to 180° with those obtained from a commercial bending sensor from Bend Labs and from a custom-built goniometer. The joint bending angles, measured by all three sensors, show good agreement with an error of less than ~1% of reading which provides a high degree of confidence in the e-textile sensor system. Subsequently, knee joint angles were measured experimentally on three subjects with each being tested three times on each of three activities (walking, running and climbing stairs). This allowed the minimum and maximum knee joint angles for each activity to be determined. This data is then used to identify activity type and perform step counting. Full article

The world’s population is aging: the expansion of the older adult population with multiple physical and health issues is now a huge socio-economic concern worldwide. Among these issues, the loss of mobility among older adults due to musculoskeletal disorders is especially serious as it has severe social, mental and physical consequences. Human body joint monitoring and early diagnosis of these disorders will be a strong and effective solution to this problem. A smart joint monitoring system can identify and record important musculoskeletal-related parameters. Such devices can be utilized for continuous monitoring of joint movements during the normal daily activities of older adults and the healing process of joints (hips, knees or ankles) during the post-surgery period. A viable monitoring system can be developed by combining miniaturized, durable, low-cost and compact sensors with the advanced communication technologies and data processing techniques. In this study, we have presented and compared different joint monitoring methods and sensing technologies recently reported. A discussion on sensors’ data processing, interpretation, and analysis techniques is also presented. Finally, current research focus, as well as future prospects and development challenges in joint monitoring systems are discussed. Full article

(1) Background: Measuring joint range of motion has traditionally occurred with a universal goniometer or expensive laboratory based kinematic analysis systems. Technological advances in wearable inertial measurement units (IMU) enables limb motion to be measured with a small portable electronic device. This paper aims to validate an IMU, the ‘Biokin’, for measuring shoulder range of motion in healthy adults; (2) Methods: Thirty participants completed four shoulder movements (forward flexion, abduction, and internal and external rotation) on each shoulder. Each movement was assessed with a goniometer and the IMU by two testers independently. The extent of agreement between each tester’s goniometer and IMU measurements was assessed with intra-class correlation coefficients (ICC) and Bland-Altman 95% limits of agreement (LOA). Secondary analysis compared agreement between tester’s goniometer or IMU measurements (inter-rater reliability) using ICC’s and LOA; (3) Results: Goniometer and IMU measurements for all movements showed high levels of agreement when taken by the same tester; ICCs > 0.90 and LOAs < ±5 degrees. Inter-rater reliability was lower; ICCs ranged between 0.71 to 0.89 and LOAs were outside a prior defined acceptable LOAs (i.e., > ±5 degrees); (4) Conclusions: The current study provides preliminary evidence of the concurrent validity of the Biokin IMU for assessing shoulder movements, but only when a single tester took measurements. Further testing of the Biokin’s psychometric properties is required before it can be confidently used in routine clinical practice and research settings. Full article