Sandra Schober, Erwin Schimbäck, Klaus Pendl, Kurt Pichler, Valentin Sturm, Frederick Runte
{"title":"利用可穿戴加速度计对腿部动作进行分类的人类活动识别系统:第一种详细方法","authors":"Sandra Schober, Erwin Schimbäck, Klaus Pendl, Kurt Pichler, Valentin Sturm, Frederick Runte","doi":"10.5194/jsss-13-187-2024","DOIUrl":null,"url":null,"abstract":"Abstract. A human activity recognition (HAR) system carried by masseurs for controlling a therapy table via different movements of legs or hip is studied. This work starts with a survey on HAR systems using the sensor position named “trouser pockets”. Afterwards, in the experiments, the impacts of different hardware systems, numbers of subjects, data generation processes (online streams/offline data snippets), sensor positions, sampling rates, sliding window sizes and shifts, feature sets, feature elimination processes, operating legs, tag orientations, classification processes (concerning method, parameters and an additional smoothing process), numbers of activities, training databases, and the use of a preceding teaching process on the classification accuracy are examined to get a thorough understanding of the variables influencing the classification quality. Besides the impacts of different adjustable parameters, this study also serves as an advisor for the implementation of classification tasks. The proposed system has three operating classes: do nothing, pump therapy table up or pump therapy table down. The first operating class consists of three activity classes (go, run, massage) such that the whole classification process exists with five classes. Finally, using online data streams, a classification accuracy of 98 % could be achieved for one skilled subject and about 90 % for one randomly chosen subject (mean of 1 skilled and 11 unskilled subjects). With the LOSO (leave-one-subject-out) technique for 12 subjects, up to 86 % can be attained. With our offline data approach, we get accuracies of 98 % for 12 subjects and up to 100 % for 1 skilled subject.\n","PeriodicalId":17167,"journal":{"name":"Journal of Sensors and Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Human activity recognition system using wearable accelerometers for classification of leg movements: a first, detailed approach\",\"authors\":\"Sandra Schober, Erwin Schimbäck, Klaus Pendl, Kurt Pichler, Valentin Sturm, Frederick Runte\",\"doi\":\"10.5194/jsss-13-187-2024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. A human activity recognition (HAR) system carried by masseurs for controlling a therapy table via different movements of legs or hip is studied. This work starts with a survey on HAR systems using the sensor position named “trouser pockets”. Afterwards, in the experiments, the impacts of different hardware systems, numbers of subjects, data generation processes (online streams/offline data snippets), sensor positions, sampling rates, sliding window sizes and shifts, feature sets, feature elimination processes, operating legs, tag orientations, classification processes (concerning method, parameters and an additional smoothing process), numbers of activities, training databases, and the use of a preceding teaching process on the classification accuracy are examined to get a thorough understanding of the variables influencing the classification quality. Besides the impacts of different adjustable parameters, this study also serves as an advisor for the implementation of classification tasks. The proposed system has three operating classes: do nothing, pump therapy table up or pump therapy table down. The first operating class consists of three activity classes (go, run, massage) such that the whole classification process exists with five classes. Finally, using online data streams, a classification accuracy of 98 % could be achieved for one skilled subject and about 90 % for one randomly chosen subject (mean of 1 skilled and 11 unskilled subjects). With the LOSO (leave-one-subject-out) technique for 12 subjects, up to 86 % can be attained. With our offline data approach, we get accuracies of 98 % for 12 subjects and up to 100 % for 1 skilled subject.\\n\",\"PeriodicalId\":17167,\"journal\":{\"name\":\"Journal of Sensors and Sensor Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Sensors and Sensor Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/jsss-13-187-2024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sensors and Sensor Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/jsss-13-187-2024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
Human activity recognition system using wearable accelerometers for classification of leg movements: a first, detailed approach
Abstract. A human activity recognition (HAR) system carried by masseurs for controlling a therapy table via different movements of legs or hip is studied. This work starts with a survey on HAR systems using the sensor position named “trouser pockets”. Afterwards, in the experiments, the impacts of different hardware systems, numbers of subjects, data generation processes (online streams/offline data snippets), sensor positions, sampling rates, sliding window sizes and shifts, feature sets, feature elimination processes, operating legs, tag orientations, classification processes (concerning method, parameters and an additional smoothing process), numbers of activities, training databases, and the use of a preceding teaching process on the classification accuracy are examined to get a thorough understanding of the variables influencing the classification quality. Besides the impacts of different adjustable parameters, this study also serves as an advisor for the implementation of classification tasks. The proposed system has three operating classes: do nothing, pump therapy table up or pump therapy table down. The first operating class consists of three activity classes (go, run, massage) such that the whole classification process exists with five classes. Finally, using online data streams, a classification accuracy of 98 % could be achieved for one skilled subject and about 90 % for one randomly chosen subject (mean of 1 skilled and 11 unskilled subjects). With the LOSO (leave-one-subject-out) technique for 12 subjects, up to 86 % can be attained. With our offline data approach, we get accuracies of 98 % for 12 subjects and up to 100 % for 1 skilled subject.
期刊介绍:
Journal of Sensors and Sensor Systems (JSSS) is an international open-access journal dedicated to science, application, and advancement of sensors and sensors as part of measurement systems. The emphasis is on sensor principles and phenomena, measuring systems, sensor technologies, and applications. The goal of JSSS is to provide a platform for scientists and professionals in academia – as well as for developers, engineers, and users – to discuss new developments and advancements in sensors and sensor systems.