Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) Field
Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human–machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the inte...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8315 |
| _version_ | 1797575173605425152 |
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| author | Tao Liu Xiangzhi Liu |
| author_facet | Tao Liu Xiangzhi Liu |
| author_sort | Tao Liu |
| collection | DOAJ |
| description | Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human–machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system’s volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development. |
| first_indexed | 2024-03-10T21:34:53Z |
| format | Article |
| id | doaj.art-a35b554d1ae344fd87a46914c112c9d3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:34:53Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a35b554d1ae344fd87a46914c112c9d32023-11-19T15:05:51ZengMDPI AGSensors1424-82202023-10-012319831510.3390/s23198315Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) FieldTao Liu0Xiangzhi Liu1The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaDue to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human–machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system’s volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development.https://www.mdpi.com/1424-8220/23/19/8315highly integrated systemnon-invasive measurementwearable systemhuman–robot interaction (HRI) |
| spellingShingle | Tao Liu Xiangzhi Liu Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) Field Sensors highly integrated system non-invasive measurement wearable system human–robot interaction (HRI) |
| title | Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) Field |
| title_full | Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) Field |
| title_fullStr | Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) Field |
| title_full_unstemmed | Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) Field |
| title_short | Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) Field |
| title_sort | perspectives in wearable systems in the human robot interaction hri field |
| topic | highly integrated system non-invasive measurement wearable system human–robot interaction (HRI) |
| url | https://www.mdpi.com/1424-8220/23/19/8315 |
| work_keys_str_mv | AT taoliu perspectivesinwearablesystemsinthehumanrobotinteractionhrifield AT xiangzhiliu perspectivesinwearablesystemsinthehumanrobotinteractionhrifield |