A Precise Hip Protection System with Multi-scale Fall Warning Algorithm Based on Offset Displacement.

Falls occur frequently in daily life and the damage to the body is irreversible. Therefore, it is crucial to implement timely and effective warning and protection systems for falls to minimize the damage caused by falls. Currently, the fall warning algorithm has shortcomings such as low recognition rates for falls and fall-risk movements and insufficient lead-time, the time before the subject impacts the floor, making it difficult for falling protection devices to function effectively. In this study, a multi-scale falls warning algorithm based on offset displacement is built, and a hip protection system is designed. The performance of the algorithm and the system is validated using 150 falling and 500 fall-risk actions from 10 volunteers. The results showed that the recognition accuracy for falling actions is 98.7% and the recognition accuracy for fall-risk actions is 99.4%, with an average lead-time of 402ms. The protection rate for falling movements reached 98.7%. This proposed algorithm and hip protection system have the potential to be applied in elderly communities, hospitals, and homes to reduce the damage caused by falls.Clinical Relevance- This study provides important reference for clinicians in analyzing fall behaviors to patients at risk of falls in clinical settings, offering valuable technical support for ensuring the safety of patients in danger of falling. It also contributes to further promoting the development of falling-prevention medical devices.

A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture classification.

Objective.Prosthetic systems are used to improve the quality of life of post-amputation patients, and research on surface electromyography (sEMG)-based gesture classification has yielded rich results. Nonetheless, current gesture classification algorithms focus on the same subject, and cross-individual classification studies that overcome physiological factors are relatively scarce, resulting in a high abandonment rate for clinical prosthetic systems. The purpose of this research is to propose an algorithm that can significantly improve the accuracy of gesture classification across individuals.Approach.Eight healthy adults were recruited, and sEMG data of seven daily gestures were recorded. A modified fuzzy granularized logistic regression (FG_LogR) algorithm is proposed for cross-individual gesture classification.Main results.The results show that the average classification accuracy of the four features based on the FG_LogR algorithm is 79.7%, 83.6%, 79.0%, and 86.1%, while the classification accuracy based on the logistic regression algorithm is 76.2%, 79.5%, 71.1%, and 81.3%, the overall accuracy improved ranging from 3.5% to 7.9%. The performance of the FG_LogR algorithm is also superior to the other five classic algorithms, and the average prediction accuracy has increased by more than 5%.Conclusion. The proposed FG_LogR algorithm improves the accuracy of cross-individual gesture recognition by fuzzy and granulating the features, and has the potential for clinical application.Significance. The proposed algorithm in this study is expected to be combined with other feature optimization methods to achieve more precise and intelligent prosthetic control and solve the problems of poor gesture recognition and high abandonment rate of prosthetic systems.

Effective evaluation of HGcnMLP method for markerless 3D pose estimation of musculoskeletal diseases patients based on smartphone monocular video.

Markerless pose estimation based on computer vision provides a simpler and cheaper alternative to human motion capture, with great potential for clinical diagnosis and remote rehabilitation assessment. Currently, the markerless 3D pose estimation is mainly based on multi-view technology, while the more promising single-view technology has defects such as low accuracy and reliability, which seriously limits clinical application. This study proposes a high-resolution graph convolutional multilayer perception (HGcnMLP) human 3D pose estimation framework for smartphone monocular videos and estimates 15 healthy adults and 12 patients with musculoskeletal disorders (sarcopenia and osteoarthritis) gait spatiotemporal, knee angle, and center-of-mass (COM) velocity parameters, etc., and compared with the VICON gold standard system. The results show that most of the calculated parameters have excellent reliability (VICON, ICC (2, k): 0.853-0.982; Phone, ICC (2, k): 0.839-0.975) and validity (Pearson r: 0.808-0.978, p<0.05). In addition, the proposed system can better evaluate human gait balance ability, and the K-means++ clustering algorithm can successfully distinguish patients into different recovery level groups. This study verifies the potential of a single smartphone video for 3D human pose estimation for rehabilitation auxiliary diagnosis and balance level recognition, and is an effective attempt at the clinical application of emerging computer vision technology. In the future, it is hoped that the corresponding smartphone program will be developed to provide a low-cost, effective, and simple new tool for remote monitoring and rehabilitation assessment of patients.

The reliability and validity of gait analysis system using 3D markerless pose estimation algorithms.

Quantifying kinematic gait for elderly people is a key factor for consideration in evaluating their overall health. However, gait analysis is often performed in the laboratory using optical sensors combined with reflective markers, which may delay the detection of health problems. This study aims to develop a 3D markerless pose estimation system using OpenPose and 3DPoseNet algorithms. Moreover, 30 participants performed a walking task. Sample entropy was adopted to study dynamic signal irregularity degree for gait parameters. Paired-sample t-test and intra-class correlation coefficients were used to assess validity and reliability. Furthermore, the agreement between the data obtained by markerless and marker-based measurements was assessed by Bland-Altman analysis. ICC (C, 1) indicated the test-retest reliability within systems was in almost complete agreement. There were no significant differences between the sample entropy of knee angle and joint angles of the sagittal plane by the comparisons of joint angle results extracted from different systems (p > 0.05). ICC (A, 1) indicated the validity was substantial. This is supported by the Bland-Altman plot of the joint angles at maximum flexion. Optical motion capture and single-camera sensors were collected simultaneously, making it feasible to capture stride-to-stride variability. In addition, the sample entropy of angles was close to the ground_truth in the sagittal plane, indicating that our video analysis could be used as a quantitative assessment of gait, making outdoor applications feasible.

A Portable Wearable Inertial System for Rehabilitation Monitoring and Evaluation of Patients With Total Knee Replacement.

Knee osteoarthritis is a degenerative disease, which greatly affects the daily life of patients. Total knee replacement (TKR) is the most common method to treat knee joint disorders and relieve knee pain. Postoperative rehabilitation exercise is the key to restore knee joint function. However, there is a lack of a portable equipment for monitoring knee joint activity and a systematic assessment scheme. We have developed a portable rehabilitation monitoring and evaluation system based on the wearable inertial unit to estimate the knee range of motion (ROM). Ten TKR patients and ten healthy adults are recruited for the experiment, then the system performance is verified by professional rehabilitation equipment Baltimore Therapeutic Equipment (BTE) Primus RS. The average absolute difference between the knee ROM and BTE Primus RS of healthy subjects and patients ranges from 0.16° to 4.94°. In addition, the knee ROM of flexion-extension and gait activity between healthy subjects and patients showed significant differences. The proposed system is reliable and effective in monitoring and evaluating the rehabilitation progress of patients. The system proposed in this work is expected to be used for long-term effective supervision of patients in clinical and dwelling environments.

Design of an Unpowered Ankle-Foot Exoskeleton Used for Walking Assistance.

Enhance human walking and running is much more difficult compared to build a machine to help someone with disability. Unpowered ankle-foot exoskeletons are the current development trend due to their lightweight, wearable, and energy-free features, but the huge recognition and energy control system still affects their practicability. To refine the recognition and control system, we designed an unpowered soft ankle-foot exoskeleton with a purely mechanical self-adaptiveness clutch, which can realize the collection and release of energy according to different gait stage. Through switching and closing of this clutch, energy is collected when the ankle is doing negative work and released when the ankle is doing positive work. Results shows the unpowered ankle-foot exoskeleton at the stiffness of 12000 N/m could relieve muscles' load, with reduction of force by 52.3 % and 5.2%, and of power by 44.2% and 7.0%, respectively for soleus and gastrocnemius in simulation.Clinical Relevance-The proposed Unpowered Ankle-Foot Exoskeleton can both reduce muscle forces and powers. Hence, it can be used to assist walking of the elderly, others with neurocognitive disorders or leg diseases.

Automatic Fall Protection for Hips Based on Micromechanical Double Gas Cylinder Rapid Puncture and Bionic Capsule Inflation.

Wearable hip-protection airbags can effectively protect hip joints when elderly people fall. This has been studied all over the world, but similar products need to use special gas cylinders and replacement of new gas cylinders needs to return to the factory; The team previously designed a mechanical puncture protection system based on standard gas cylinders and standard threaded interfaces, but the airbag still has shortcomings such as the small protective area caused by a single gas cylinder. To solve the above problems, a set of wearable hip automatic protection systems based on micromechanical double gas cylinder rapid puncture (MDGCRP) is now designed. Through a large number of experiments, it was found that the response time of MDGCRP was 92ms and the execution time was 177.5ms. Compared with the single gas cylinder approach, the airbag provides greater protection to the hip while the filling time and module weight remain essentially unchanged. The system is triggered by physical and mechanical methods. Compared with chemical blasting or hot-melt methods, the system has the characteristics of low cost and consumables that can be safely and easily replaced by themselves.

A novel gait parameter estimation method for healthy adults and postoperative patients with an ear-worn sensor.

OBJECTIVE: Gait analysis helps to assess recovery during rehabilitation. Previous gait analysis studies are primarily applicable to healthy subjects or to postoperative patients. The purpose of this paper is to construct a new gait parameter estimation platform based on an ear-worn activity recognition (e-AR) sensor, which can be used for both normal and pathological gait signals. APPROACH: Thirty healthy adults and eight postoperative patients participated in the experiment. A method based on singular spectrum analysis (SSA) and iterative mean filtering (IMF) is proposed to detect gait events and estimate three key gait parameters, i.e. stride time, swing time, and stance time. MAIN RESULTS: Experimental results show that the estimated gait parameters provided by the proposed method are very close to the gait parameters provided by the gait assessment system. For normal gait signals, the average absolute errors of stride, swing, and stance time are 27.8 ms, 35.8 ms, and 37.5 ms, respectively. For pathological gait signals, the average absolute error of stride time is 32.1 ms. SIGNIFICANCE: The proposed parameter estimation method can be applied to both general analysis for healthy subjects and rehabilitation evaluation for postoperative patients. The convenience and comfort of the ear-worn sensor increase its potential for practical applications.