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.

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.

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.

Pre-impact Alarm System for Fall Detection Using MEMS Sensors and HMM-based SVM Classifier.

Accidental fall can cause physical injury, fracture and other health complication, especially for elderly people living alone. Aimed to provide timely assistance after the occurrence of falling down, a pre-fall alarm system was proposed. In order to test the reliability of pre-fall alarm system, eighteen subjects who worn this device on the waist were required to participate in a series of experiments. The acceleration and angular velocity time series extracted from human motion processes were used to described human motion features. HMM-based SVM classifier was used to determine the maximum separation boundary between fall and Activities of Daily Living (ADLs). The fall detection results showed 94.91% accuracy, 97.22% Sensitivity and 93.75% Specificity. The proposed device can accurately recognize fall event, achieve additional functions, and have advantages of small size and low power consumption. Based on the findings, this pre-impact fall alarm system with detection algorithm could potentially be useful for monitoring the state of physical function in elderly population.

Real-time Action Recognition and Fall Detection Based on Smartphone.

This paper presents a smartphone application which has realized action recognition and fall detection. The application identifies the holding pattern of smartphone by the data of light sensor, distance sensor and accelerometer sensor, which reduce the impact of recognition resulting from the smartphone's different positions. And then the application uses data collected from the acceleration sensor, the direction angle sensor and the gyro sensor to distinguish fall from daily actions. The results of human motion recognition are uploaded to the server. For the purpose of real time, the network stability of the application is improved by the method of multi-layer detection based on heartbeat packet. Experiments prove that the way of improving network stability can reduce the rate of losing packet. The accuracy of action recognition achieves more than 90%.

Unexpected catalytic performance of Fe-M-C (M = N, P, and S) electrocatalysts towards oxygen reduction reaction: surface heteroatoms boost the activity of Fe2M/graphene nanocomposites.

The rational design of non-noble materials as low-cost, highly efficient, and durable catalysts to improve the oxygen reduction reaction is extremely urgent and challenging. The oxygen reduction reaction is a kinetically sluggish process that greatly affects the energy conversion efficiency. In this paper, novel hierarchical heteroatoms-co-doped Fe2M/graphene (M = P, N) nanocomposites were developed by a facile strategy, including hydrothermal and subsequent calcination methods. The thermal treatment of an ionic liquid and thiourea not only supplied heteroatom sources but also promoted the formation of iron phosphide and iron nitride and enhanced their catalytic performances. The electrochemical results indicated that the as-obtained hybrid catalysts manifested enhanced electrocatalytic activity toward the oxygen reduction reaction owing to the strong synergistic effects. The high content of heteroatoms distributed on the surface and interface of the hybrids and the density functional theory calculations suggested that Fe-N-C, Fe-P-C, and Fe-S-C multiple active surface sites were formed at the hybrids interfaces. Moreover, these results demonstrated that heteroatom-doped catalysts could effectively form a charge-transfer channel and thus modify the charge distribution in the hybrids interfaces. The as-prepared heteroatoms-doped Fe2M/graphene hybrids would be developed into highly efficient catalysts as ideal alternatives for noble metal catalysts in practical applications.

Phosphorus and Fluorine Co-Doping Induced Enhancement of Oxygen Evolution Reaction in Bimetallic Nitride Nanorods Arrays: Ionic Liquid-Driven and Mechanism Clarification.

Electrocatalytic splitting of water is becoming increasingly crucial for renewable energy and device technologies. As one of the most important half-reactions for water splitting reactions, the oxygen evolution reaction (OER) is a kinetically sluggish process that will greatly affect the energy conversion efficiency. Therefore, exploring a highly efficient and durable catalyst to boost the OER is of great urgency. In this work, we develop a facile strategy for the synthesis of well-defined phosphorus and fluorine co-doped Ni1.5 Co1.5 N hybrid nanorods (HNs) by using ionic liquids (ILs; 1-butyl-3-methylimidazolium hexafluorophosphate). In comparison to the IrO2 catalyst, the as-obtained PF/Ni1.5 Co1.5 N HNs manifests a low overpotential of 280 mV at 10 mA cm-2 , Tafel slope of 66.1 mV dec-1 , and excellent durability in 1.0 m KOH solution. Furthermore, the iR-corrected electrochemical results indicate it could achieve a current density of 100 mA cm-2 at an overpotential of 350 mV. The combination of cobalt and nickel elements, 1D mesoporous nanostructure, heteroatom incorporation, and ionic liquid-assisted nitridation, which result in faster charge transfer capability and more active surface sites, can facilitate the release of oxygen bubbles from the catalyst surface. Our findings confirm that surface heteroatom doping in bimetallic nitrides could serve as a new class of OER catalyst with excellent catalytic activity.

Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms.

The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.

Exploration and comparison of the pre-impact lead time of active and passive falls based on inertial sensors.

Research on falls in elderly people has a great social significance because of the rapidly growing of the aging population. The pre-impact lead time of fall (PLT) is an important part of the human fall theory. PLT is the longest time for a person who is going to fall to take action in order to prevent the fall or to reduce bodily injuries from the fall impact. However, there is no clear definition of PLT so far. There is also no comparative study for active and passive falls. In this study, we proposed a theoretical definition of the PLT, based on a new method of fall event division. We also compared the differences of PLT and the related angles between active and passive falls. Eight healthy adult subjects were arranged to perform three kinds of activities of daily living (sitting, walking and lying), and two kinds fall activities (active and passive) in three directions (forward, backward and lateral fall). Nine inertial sensor modules were used to measure the body segmental kinematic characteristics of each subject in our experimental activities. In this paper, a fall event was suggested to divide into three or four phases and then the critical phase could be divided into three periods (pre-impact, impact, and post-impact). Two fall models were developed for active and passive falls using acceleration data. The average value of PLT for active falls is about 514 ± 112 ms and it is smaller than the value for passive falls, which is 731 ± 104 ms. The longest PLTs were measured on the chest or waist instead of other locations, such as the thigh and shank. The PLTs of the three kinds of fall activities were slightly different, but there was a significant difference between two fall modes. The PLT showed the correlation to the body angle at the start of PLT, but it was uncorrelated at the end of PLT. The angles at the start of PLT had slight variations (<10 degrees) from the steady standing state except in passive forward falls (max 16 degrees) due to the self-control. The landing angles were significantly different in the both fall modes in all the three directions of fall, indicating the state of the trunk was uncertain when the hip contacted the ground. It can be concluded that it is feasible to prevent falls by using an early pre-impact fall alarm device; the present study provides important reference for development of pre-impact fall alarm devices.