16例脊髓损伤的康复治疗分析

为观察康复治疗对脊髓损伤患者功能的影响 ,对 1 6例脊髓损伤患者进行了 1 9周针对性的康复治疗 ,治疗前后采用Barthel指数法对患者的日常生活活动 (ADL)能力进行了评定。结果表明 ,治疗后 1 6例患者的ADL评分均明显高于康复治疗前 (P <0 0 1 )。提示针对脊髓损伤所致功能障碍的特点 ,采取相应的康复治疗和训练有助于提高患者日常生活活动能力     

聪脑益智针法联合精神运动康复 改善脑瘫认知障碍临床研究

摘 要:目的 观察聪脑益智针法联合精神运动康复改善脑瘫患儿认知功能障碍的临床疗效。方法 选取我院住院脑瘫患儿伴有认知功能障碍者100例为研究对象，以随机数字表法分为综合康复组、聪脑益智针法联合精神运动康复训练组各50例。两组患儿均以综合康复治疗为基础，治疗组在综合康复治疗基础上进行聪脑益智针刺联合精神运动康复训练治疗，两组患儿均治疗3个月，治疗前后均按参照有关文献拟定的疗效标准进行评定观察。结果 应用聪脑益智针法联合精神运动康复训练治疗脑瘫患儿认知功能障碍在提高智力方面亦优于常规综合治疗组，差异有统计学意义（P＜0.05）。结论 聪脑益智针法联合精神运动康复可有效改善脑瘫患儿认知障碍，临床疗效明显，值得在临床上推广应用。     

Biodegradable Shape-Memory Ionogels as Green and Adaptive Wearable Electronics Toward Physical Rehabilitation

Rehabilitation is necessary for the recovery of patients with paralysis caused by stroke and muscle atrophy. Wearable electronics can provide feedback on physical training and facilitate healthcare. However, most existing wearable electronics are difficult to maintain a conformal skin-device interface. Additionally, the use of non-degradable electronic materials is associated with environmental risks. Herein, ionogels with biodegradation and shape-memory properties as eco-friendly and geometry-adaptive wearable electronics for rehabilitation are proposed. The biodegradation is enabled by incorporating polycaprolactone segments into the ionogel matrix. Moreover, the ionogel-based wearable electronics can be conformal to certain joints by shape programming, and provide stable and reproducible real-time signals reflecting joint movements during long-term rehabilitation training assisted by a robotic glove, facilitating carers to assess rehabilitation efficacy and choose an appropriate scheme. This study demonstrates the potential of biodegradable shape-memory ionogels as green and adaptive wearable electronics for robot-assisted rehabilitation.     

Factors affecting real-time evaluation of muscle function in smart rehab systems

Advancements in remote medical technologies and smart devices have led to expectations of contactless rehabilitation. Conventionally, rehabilitation requires clinicians to perform routine muscle function assessments with patients. However, assessment results are difficult to cross-reference owing to the lack of a gold standard. Thus, the application of remote smart rehabilitation systems is significantly hindered. This study analyzes the factors affecting the real-time evaluation of muscle function based on biometric sensor data so that we can provide a basis for a remote system. We acquired real clinical stroke patient data to identify the meaningful features associated with normal and abnormal musculature. We provide a system based on these emerging features that assesses muscle functionality in real time via streamed biometric signal data. A system view based on the amount of data, data processing speed, and feature proportions is provided to support the production of a rudimentary remote smart rehabilitation system.     

A Self-Powered Body Motion Sensing Network Integrated with Multiple Triboelectric Fabrics for Biometric Gait Recognition and Auxiliary Rehabilitation Training

Gait analysis provides a convenient strategy for the diagnosis and rehabilitation assessment of diseases of skeletal, muscular, and neurological systems. However, challenges remain in current gait recognition methods due to the drawbacks of complex systems, high cost, affecting natural gait, and one-size-fits-all model. Here, a highly integrated gait recognition system composed of a self-powered multi-point body motion sensing network (SMN) based on full textile structure is demonstrated. By combining of newly developed energy harvesting technology of triboelectric nanogenerator (TENG) and traditional textile manufacturing process, SMN not only ensures high pressure response sensitivity up to 1.5 V kPa⁻¹, but also is endowed with several good properties, such as full flexibility, excellent breathability (165 mm s⁻¹), and good moisture permeability (318 g m⁻² h⁻¹). By using machine learning to analyze periodic signals and dynamic parameters of limbs swing, the gait recognition system exhibits a high accuracy of 96.7% of five pathological gaits. In addition, a customizable auxiliary rehabilitation exercise system that monitors the extent of the patient's rehabilitation exercise is developed to observe the patient's condition and instruct timely recovery training. The machine learning-assisted SMN can provide a feasible solution for disease diagnosis and personalized rehabilitation of the patients.     

An original mechatronic design of a kinaesthetic hand exoskeleton for virtual reality-based applications

Within the new industrial era, the interaction between humans and virtual reality is spreading across our lives. The development of exoskeleton designed to enhance the immersivity of virtual reality environments has a potentially considerable social impact and arises as a hot research topic. The presented work dwells well with the subject by describing the mechatronic design process of a kinaesthetic hand exoskeleton system meant to reproduce proprioceptive stimuli coming from the interaction with a virtual reality. The presented prototype is a modular device, equipped with force and pose sensors, and driven by a Bowden-cable-based remote actuation system. Unlike similar devices, the proposed exoskeleton is specifically thought for VR interaction and is designed to be reversible while exerting up to 15 N per finger. For a more accurate rendering of kinetostatic finger stimuli, a procedure for reconstructing HMI force as a function of measured force and position signals by employing a system’s kinematic and dynamic model is presented, detailed, and followed by some preliminary tests. The results showed that the model can trace forces back to the end-effector with a percentage error below 15%.