Human Activity Recognition(HAR)has always been a difficult task to tackle.It is mainly used in security surveillance,human-computer interaction,and health care as an assistive or diagnostic technology in combination w...Human Activity Recognition(HAR)has always been a difficult task to tackle.It is mainly used in security surveillance,human-computer interaction,and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things(IoT).Human Activity Recognition data can be recorded with the help of sensors,images,or smartphones.Recognizing daily routine-based human activities such as walking,standing,sitting,etc.,could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network(2D CNN)MODEL,Long Short Term Memory(LSTM)Model,Bidirectional long short-term memory(Bi-LSTM)are used for the classification.It has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate,with almost all activities accurately getting recognized over 90%of the time.Furthermore,because all the examples are generated from only 20 s of data,these actions can be recognised fast.Apart from classification,the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy(CP)for the evaluation of chosen Spatio-temporal features based on 3D foot trajectory.Case-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 kg.Optical Motion Capture(OMC)equipment was used as the referral method to assess the functionality and quality of the foot-worn device.The average accuracy±precision for stride length,cadence,and step length was 3.5±4.3,4.1±3.8,and 0.6±2.7 cm respectively.For cadence,stride length,swing,and step length,people with CP had considerably high inter-stride variables.Foot-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.展开更多
Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characte...Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characteristics that help to define normalcy.Swiftly identifying such characteristics that are difficult to spot by the naked eye,can help in monitoring the elderly who require constant care and support.Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait.It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical diagnosis.Gait images made publicly available by the Chinese Academy of Sciences(CASIA)Gait Database was used in this study.After evaluating using the CASIA B and C datasets,this paper proposes a Convolutional Neural Network(CNN)and a CNN Long Short-TermMemory Network(CNN-LSTM)model for classifying the gait silhouette images.Transfer learningmodels such as MobileNetV2,InceptionV3,Visual Geometry Group(VGG)networks such as VGG16 and VGG19,Residual Networks(ResNet)like the ResNet9 and ResNet50,were used to compare the efficacy of the proposed models.CNN proved to be the best by achieving the highest accuracy of 94.29%.This was followed by ResNet9 and CNN-LSTM,which arrived at 93.30%and 87.25%accuracy,respectively.展开更多
文摘Human Activity Recognition(HAR)has always been a difficult task to tackle.It is mainly used in security surveillance,human-computer interaction,and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things(IoT).Human Activity Recognition data can be recorded with the help of sensors,images,or smartphones.Recognizing daily routine-based human activities such as walking,standing,sitting,etc.,could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network(2D CNN)MODEL,Long Short Term Memory(LSTM)Model,Bidirectional long short-term memory(Bi-LSTM)are used for the classification.It has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate,with almost all activities accurately getting recognized over 90%of the time.Furthermore,because all the examples are generated from only 20 s of data,these actions can be recognised fast.Apart from classification,the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy(CP)for the evaluation of chosen Spatio-temporal features based on 3D foot trajectory.Case-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 kg.Optical Motion Capture(OMC)equipment was used as the referral method to assess the functionality and quality of the foot-worn device.The average accuracy±precision for stride length,cadence,and step length was 3.5±4.3,4.1±3.8,and 0.6±2.7 cm respectively.For cadence,stride length,swing,and step length,people with CP had considerably high inter-stride variables.Foot-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.
文摘Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characteristics that help to define normalcy.Swiftly identifying such characteristics that are difficult to spot by the naked eye,can help in monitoring the elderly who require constant care and support.Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait.It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical diagnosis.Gait images made publicly available by the Chinese Academy of Sciences(CASIA)Gait Database was used in this study.After evaluating using the CASIA B and C datasets,this paper proposes a Convolutional Neural Network(CNN)and a CNN Long Short-TermMemory Network(CNN-LSTM)model for classifying the gait silhouette images.Transfer learningmodels such as MobileNetV2,InceptionV3,Visual Geometry Group(VGG)networks such as VGG16 and VGG19,Residual Networks(ResNet)like the ResNet9 and ResNet50,were used to compare the efficacy of the proposed models.CNN proved to be the best by achieving the highest accuracy of 94.29%.This was followed by ResNet9 and CNN-LSTM,which arrived at 93.30%and 87.25%accuracy,respectively.
