In this paper, the finger muscular forces were estimated and analyzed through the application of inverse dynamics-based static optimization, and a hand exoskeleton system was designed to pull the fingers and measure t...In this paper, the finger muscular forces were estimated and analyzed through the application of inverse dynamics-based static optimization, and a hand exoskeleton system was designed to pull the fingers and measure the dynamics of the hand. To solve the static optimization, a muscular model of the hand flexors was derived. The experimental protocol was devised to analyze finger flexors in order to evaluate spasticity of the clenched fingers; muscular forces were estimated while the flexed fingers were extended by the exoskeleton with external loads applied. To measure the finger joint angles, the hand exoskeleton system was designed using four-bar linkage structure and potentiometers. In addition, the external loads to the fingertips were generated by cable driven actuators and simultaneously measured by loadcells which were located at each phalanx. The ex- periments were performed with a normal person and the muscular forces estimation results were discussed with reference to the physical phenomena.展开更多
Flexible thermoelectric materials play an important role in smart wearables,such as wearable power generation,self-powered sensing,and personal thermal management.However,with the rapid development of Internet of Thin...Flexible thermoelectric materials play an important role in smart wearables,such as wearable power generation,self-powered sensing,and personal thermal management.However,with the rapid development of Internet of Things(IoT)and artificial intelligence(AI),higher standards for comfort,multifunctionality,and sustainable operation of wearable electronics have been proposed,and it remains challenging to meet all the requirements of currently reported thermoelectric devices.Herein,we present a multifunctional,wearable,and wireless sensing system based on a thermoelectric knitted fabric with over 600 mm·s^(-1)air permeability and a stretchability of 120%.The device coupled with a wireless transmission system realizes self-powered monitoring of human respiration through an mobile phone application(APP).Furthermore,an integrated thermoelectric system was designed to combine photothermal conversion and passive radiative cooling,enabling the characteristics of being powered by solar-driven in-plane temperature differences and monitoring outdoor sunlight intensity through the APP.Additionally,we decoupled the complex signals of resistance and thermal voltage during deformation under solar irradiation based on the anisotropy of the knitted fabrics to enable the device to monitor and optimize the outdoor physical activity of the athlete via the APP.This novel thermoelectric fabricbased wearable and wireless sensing platform has promising applications in next-generation smart textiles.展开更多
背景日常活动量减少和运动功能受限是心力衰竭患者的特征性表现之一,体位/体动信息与心衰患者疾病严重程度和预后密切相关。通过可穿戴生理监测系统量化体位/体动信息或可作为一种潜在的心衰病情严重程度定量评价手段,其与纽约心脏病协...背景日常活动量减少和运动功能受限是心力衰竭患者的特征性表现之一,体位/体动信息与心衰患者疾病严重程度和预后密切相关。通过可穿戴生理监测系统量化体位/体动信息或可作为一种潜在的心衰病情严重程度定量评价手段,其与纽约心脏病协会(New York Heart Association,NYHA)心功能分级的关系需进一步研究。目的探讨心衰患者体位/体动信息定量分析结果与NYHA分级的相关性。方法纳入2021年5月—2022年11月在四川大学华西医院心内科住院的心衰患者,通过可穿戴生理监测系统采集患者入院当天和出院前1 d各24 h的连续生理监测数据,同步收集临床数据。通过对可穿戴生理监测系统内的三轴加速传感器信息进行处理分析,计算卧床时间、活动时间、步数、睡眠翻身次数4个体位/体动指标。基于患者入院时NYHA分级、入院和出院情况、出院时NYHA分级改善与否进行分组,分析体位/体动指标与NYHA分级的关联性。结果纳入心衰患者69例,平均年龄(60.90±14.24)岁,其中男性40例,NYHAⅡ、Ⅲ、Ⅳ级的患者分别有9例、24例、36例。随着NYHA分级的升高,心衰患者全天的卧床时间占比逐渐增多,而全天的活动时间占比、平均每小时步数逐渐降低,以上3个指标在NYHAⅡ、Ⅲ、Ⅳ级间均有统计学差异(P均<0.05);其中卧床时间占比(r_(s)=0.319,P=0.008)与NYHA分级呈正相关,活动时间占比(r_(s)=-0.312,P=0.009)、平均每小时步数(r_(s)=-0.309,P=0.010)与NYHA分级存在负相关。出院时的卧床时间占比显著低于入院时(96.25%vs 97.63%,P=0.026);出院时的活动时间占比显著高于入院时(3.32%vs 1.78%,P<0.001);出院时的平均每小时步数显著高于入院时(97.17步/h vs 35.58步/h,P<0.001);其中出院时NYHA改善组患者的体位/体动指标变化趋势同上,未改善组仅出院时的平均每小时步数显著高于入院时,NYHA改善组的出入院平均每小时步数变化值�展开更多
文摘In this paper, the finger muscular forces were estimated and analyzed through the application of inverse dynamics-based static optimization, and a hand exoskeleton system was designed to pull the fingers and measure the dynamics of the hand. To solve the static optimization, a muscular model of the hand flexors was derived. The experimental protocol was devised to analyze finger flexors in order to evaluate spasticity of the clenched fingers; muscular forces were estimated while the flexed fingers were extended by the exoskeleton with external loads applied. To measure the finger joint angles, the hand exoskeleton system was designed using four-bar linkage structure and potentiometers. In addition, the external loads to the fingertips were generated by cable driven actuators and simultaneously measured by loadcells which were located at each phalanx. The ex- periments were performed with a normal person and the muscular forces estimation results were discussed with reference to the physical phenomena.
基金supported by the National Natural Science Foundation of China(51973027 and 52003044)the Fundamental Research Funds for the Central Universities(2232020A-08)+4 种基金International Cooperation Fund of Science and Technology Commission of Shanghai Municipality(21130750100)the Major Scientific and Technological Innovation Projects of Shandong Province(2021CXGC011004)supported by the Chang Jiang Scholars Program and the Innovation Program of Shanghai Municipal Education Commission(2019-01-07-00-03-E00023)to Prof.Xiaohong Qinthe State Key Laboratory for Modification of Chemical Fibers and Polymer Materials(KF2216)and Donghua University(DHU)Distinguished Young Professor Program to Prof.Liming Wangthe Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University(CUSF-DH-D-2022040)to Xinyang He.
文摘Flexible thermoelectric materials play an important role in smart wearables,such as wearable power generation,self-powered sensing,and personal thermal management.However,with the rapid development of Internet of Things(IoT)and artificial intelligence(AI),higher standards for comfort,multifunctionality,and sustainable operation of wearable electronics have been proposed,and it remains challenging to meet all the requirements of currently reported thermoelectric devices.Herein,we present a multifunctional,wearable,and wireless sensing system based on a thermoelectric knitted fabric with over 600 mm·s^(-1)air permeability and a stretchability of 120%.The device coupled with a wireless transmission system realizes self-powered monitoring of human respiration through an mobile phone application(APP).Furthermore,an integrated thermoelectric system was designed to combine photothermal conversion and passive radiative cooling,enabling the characteristics of being powered by solar-driven in-plane temperature differences and monitoring outdoor sunlight intensity through the APP.Additionally,we decoupled the complex signals of resistance and thermal voltage during deformation under solar irradiation based on the anisotropy of the knitted fabrics to enable the device to monitor and optimize the outdoor physical activity of the athlete via the APP.This novel thermoelectric fabricbased wearable and wireless sensing platform has promising applications in next-generation smart textiles.
文摘背景日常活动量减少和运动功能受限是心力衰竭患者的特征性表现之一,体位/体动信息与心衰患者疾病严重程度和预后密切相关。通过可穿戴生理监测系统量化体位/体动信息或可作为一种潜在的心衰病情严重程度定量评价手段,其与纽约心脏病协会(New York Heart Association,NYHA)心功能分级的关系需进一步研究。目的探讨心衰患者体位/体动信息定量分析结果与NYHA分级的相关性。方法纳入2021年5月—2022年11月在四川大学华西医院心内科住院的心衰患者,通过可穿戴生理监测系统采集患者入院当天和出院前1 d各24 h的连续生理监测数据,同步收集临床数据。通过对可穿戴生理监测系统内的三轴加速传感器信息进行处理分析,计算卧床时间、活动时间、步数、睡眠翻身次数4个体位/体动指标。基于患者入院时NYHA分级、入院和出院情况、出院时NYHA分级改善与否进行分组,分析体位/体动指标与NYHA分级的关联性。结果纳入心衰患者69例,平均年龄(60.90±14.24)岁,其中男性40例,NYHAⅡ、Ⅲ、Ⅳ级的患者分别有9例、24例、36例。随着NYHA分级的升高,心衰患者全天的卧床时间占比逐渐增多,而全天的活动时间占比、平均每小时步数逐渐降低,以上3个指标在NYHAⅡ、Ⅲ、Ⅳ级间均有统计学差异(P均<0.05);其中卧床时间占比(r_(s)=0.319,P=0.008)与NYHA分级呈正相关,活动时间占比(r_(s)=-0.312,P=0.009)、平均每小时步数(r_(s)=-0.309,P=0.010)与NYHA分级存在负相关。出院时的卧床时间占比显著低于入院时(96.25%vs 97.63%,P=0.026);出院时的活动时间占比显著高于入院时(3.32%vs 1.78%,P<0.001);出院时的平均每小时步数显著高于入院时(97.17步/h vs 35.58步/h,P<0.001);其中出院时NYHA改善组患者的体位/体动指标变化趋势同上,未改善组仅出院时的平均每小时步数显著高于入院时,NYHA改善组的出入院平均每小时步数变化值�
