Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action
Abstract A system called WiSDP, which is based on Wi‐Fi signals, to detect whether a Seated Dumbbell Press action is standard by using inexpensive consumer Wi‐Fi devices is proposed. Compared with the scheme based on high speed cameras and wearable sensors, Wi‐Fi devices are insensitive to light and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-03-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12093 |
| _version_ | 1811183622277824512 |
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| author | Yuxi Qin Su Pan Weiwei Zhou Duowei Pan Zibo Li |
| author_facet | Yuxi Qin Su Pan Weiwei Zhou Duowei Pan Zibo Li |
| author_sort | Yuxi Qin |
| collection | DOAJ |
| description | Abstract A system called WiSDP, which is based on Wi‐Fi signals, to detect whether a Seated Dumbbell Press action is standard by using inexpensive consumer Wi‐Fi devices is proposed. Compared with the scheme based on high speed cameras and wearable sensors, Wi‐Fi devices are insensitive to light and colour, do not need wear any device, and decrease the risk of disclosing privacy. WiSDP senses environment changes through the Channel State Information which is fine‐grained physical layer information comparing to frequently used Received Signal Strength Indicator. Compared to the action recognition, action quality recognition depends on slight differences between a non‐standard action and standard actions, which makes it challenging. The authors propose an improved sliding window algorithm calculating action energy to extract Seated Dumbbell Press actions from the Channel State Information streams, estimate action quality by choosing an appropriate classifier and use Principal Component Analysis and Butterworth low‐pass filter to remove noise. The authors conduct experiments in two different scenarios and the average true positive rate of WiSDP are 94.66% and 95.11%, respectively. |
| first_indexed | 2024-04-11T09:50:02Z |
| format | Article |
| id | doaj.art-b97fec0b4bae449f9fb41069232a3d0b |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-04-11T09:50:02Z |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-b97fec0b4bae449f9fb41069232a3d0b2022-12-22T04:30:50ZengWileyIET Communications1751-86281751-86362021-03-0115461362610.1049/cmu2.12093Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press actionYuxi Qin0Su Pan1Weiwei Zhou2Duowei Pan3Zibo Li4Key Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education Nanjing University of Posts and Telecommunications Nanjing ChinaKey Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education Nanjing University of Posts and Telecommunications Nanjing ChinaKey Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education Nanjing University of Posts and Telecommunications Nanjing ChinaCollege of Letter & Science University of California Berkeley California USAKey Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education Nanjing University of Posts and Telecommunications Nanjing ChinaAbstract A system called WiSDP, which is based on Wi‐Fi signals, to detect whether a Seated Dumbbell Press action is standard by using inexpensive consumer Wi‐Fi devices is proposed. Compared with the scheme based on high speed cameras and wearable sensors, Wi‐Fi devices are insensitive to light and colour, do not need wear any device, and decrease the risk of disclosing privacy. WiSDP senses environment changes through the Channel State Information which is fine‐grained physical layer information comparing to frequently used Received Signal Strength Indicator. Compared to the action recognition, action quality recognition depends on slight differences between a non‐standard action and standard actions, which makes it challenging. The authors propose an improved sliding window algorithm calculating action energy to extract Seated Dumbbell Press actions from the Channel State Information streams, estimate action quality by choosing an appropriate classifier and use Principal Component Analysis and Butterworth low‐pass filter to remove noise. The authors conduct experiments in two different scenarios and the average true positive rate of WiSDP are 94.66% and 95.11%, respectively.https://doi.org/10.1049/cmu2.12093Filtering methods in signal processingComputer communicationsRadio links and equipmentDigital signal processingComputer vision and image processing techniquesLocal area networks |
| spellingShingle | Yuxi Qin Su Pan Weiwei Zhou Duowei Pan Zibo Li Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action IET Communications Filtering methods in signal processing Computer communications Radio links and equipment Digital signal processing Computer vision and image processing techniques Local area networks |
| title | Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action |
| title_full | Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action |
| title_fullStr | Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action |
| title_full_unstemmed | Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action |
| title_short | Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action |
| title_sort | consumer wi fi device based action quality recognition an illustrative example of seated dumbbell press action |
| topic | Filtering methods in signal processing Computer communications Radio links and equipment Digital signal processing Computer vision and image processing techniques Local area networks |
| url | https://doi.org/10.1049/cmu2.12093 |
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