目的:研究无错性学习联合n-back训练对脑外伤后记忆功能的影响。方法:60例脑外伤患者分为3组,各20例,分别为对照组无错性学习进行常规记忆训练(1组)、n-back训练(2组)和观察组无错性学习联合n-back训练,均治疗4周。在治疗前、治疗后采...目的:研究无错性学习联合n-back训练对脑外伤后记忆功能的影响。方法:60例脑外伤患者分为3组,各20例,分别为对照组无错性学习进行常规记忆训练(1组)、n-back训练(2组)和观察组无错性学习联合n-back训练,均治疗4周。在治疗前、治疗后采用简易精神状态检查量表(Mini-mental State Examination,MMSE)、蒙特利乐认知功能评估(Montreal Cognitive Assessment,MOCA)、修订韦氏成人记忆量表(WMS-RC)进行双盲评定,比较3组的认知功能和记忆功能。结果:3组治疗前的MMSE、MOCA、WMS-RC评分组间差异无统计学意义(P>0.05),3组治疗后的MMSE、MOCA、WMS-RC评分均高于治疗前(t=9.245,6.090,13.623;5.320,6.090,15.497;6.736,5.686,10.513;P<0.05),且观察组的MMSE、MOCA、WMS-RC分值变化均高于对照组1组、2组,差异有统计学意义(F=9.696,14.804,5.420;P<0.05)。结论:对脑外伤后记忆障碍患者采用无错性学习联合n-back训练能有效改善记忆功能障碍。展开更多
Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring information.Healthcare systems depend on multi-level data for detecting illnesses and consequ...Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring information.Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures.The collection of WS data and integration of that data for diagnostic purposes is a difficult task.This paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition accuracy.The research is based on a case study in a health organization.With the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness.As a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic efficiency.Sensor breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect suggestions.This paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance systems.Features of WS data are examined extensively using active and iterative learning to identify errors in specific postures.This technology improves position detection accuracy,analysis duration,and error rate,regardless of user movements.Wearable devices play a critical role in the management and treatment of patients.They can ensure that patients are provided with a unique treatment for their medical needs.This paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature analysis.At first,the patients’walking patterns are tracked at various time intervals.The characteristics are then evaluated in relation to the stored data using a random forest classifier.展开更多
文摘目的:研究无错性学习联合n-back训练对脑外伤后记忆功能的影响。方法:60例脑外伤患者分为3组,各20例,分别为对照组无错性学习进行常规记忆训练(1组)、n-back训练(2组)和观察组无错性学习联合n-back训练,均治疗4周。在治疗前、治疗后采用简易精神状态检查量表(Mini-mental State Examination,MMSE)、蒙特利乐认知功能评估(Montreal Cognitive Assessment,MOCA)、修订韦氏成人记忆量表(WMS-RC)进行双盲评定,比较3组的认知功能和记忆功能。结果:3组治疗前的MMSE、MOCA、WMS-RC评分组间差异无统计学意义(P>0.05),3组治疗后的MMSE、MOCA、WMS-RC评分均高于治疗前(t=9.245,6.090,13.623;5.320,6.090,15.497;6.736,5.686,10.513;P<0.05),且观察组的MMSE、MOCA、WMS-RC分值变化均高于对照组1组、2组,差异有统计学意义(F=9.696,14.804,5.420;P<0.05)。结论:对脑外伤后记忆障碍患者采用无错性学习联合n-back训练能有效改善记忆功能障碍。
文摘Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring information.Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures.The collection of WS data and integration of that data for diagnostic purposes is a difficult task.This paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition accuracy.The research is based on a case study in a health organization.With the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness.As a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic efficiency.Sensor breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect suggestions.This paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance systems.Features of WS data are examined extensively using active and iterative learning to identify errors in specific postures.This technology improves position detection accuracy,analysis duration,and error rate,regardless of user movements.Wearable devices play a critical role in the management and treatment of patients.They can ensure that patients are provided with a unique treatment for their medical needs.This paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature analysis.At first,the patients’walking patterns are tracked at various time intervals.The characteristics are then evaluated in relation to the stored data using a random forest classifier.
