Human Activity Vibrations
We present a unique, comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus, or a taxi. The measurements are carried out by embedded sensor accelerometers in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in vari...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Data |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5729/6/10/104 |
| _version_ | 1797514842891878400 |
|---|---|
| author | Sakdirat Kaewunruen Jessada Sresakoolchai Junhui Huang Satoru Harada Wisinee Wisetjindawat |
| author_facet | Sakdirat Kaewunruen Jessada Sresakoolchai Junhui Huang Satoru Harada Wisinee Wisetjindawat |
| author_sort | Sakdirat Kaewunruen |
| collection | DOAJ |
| description | We present a unique, comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus, or a taxi. The measurements are carried out by embedded sensor accelerometers in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they perform the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertically stored in an Excel Macro-enabled Workbook (xlsm) format that can be used to train an AI model in a smartphone which has the potential to collect people’s vibration data and decide what movement is being conducted. Moreover, with more data received, the database can be updated and used to train the model with a larger dataset. The prevalence of the smartphone opens the door to crowdsensing, which leads to the pattern of people taking public transport being understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transport, services and schedules can be planned perceptively. |
| first_indexed | 2024-03-10T06:37:17Z |
| format | Article |
| id | doaj.art-1da09c73c9f54e4dba7ae20dbfda56b0 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T06:37:17Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-1da09c73c9f54e4dba7ae20dbfda56b02023-11-22T17:55:37ZengMDPI AGData2306-57292021-09-0161010410.3390/data6100104Human Activity VibrationsSakdirat Kaewunruen0Jessada Sresakoolchai1Junhui Huang2Satoru Harada3Wisinee Wisetjindawat4School of Engineering, University of Birmingham, Birmingham B15 2TT, UKSchool of Engineering, University of Birmingham, Birmingham B15 2TT, UKSchool of Engineering, University of Birmingham, Birmingham B15 2TT, UKHitachi Europe Limited, ERD Office, 12th Floor, 125 London Wall, London EC2Y 5AL, UKHitachi Europe Limited, ERD Office, 12th Floor, 125 London Wall, London EC2Y 5AL, UKWe present a unique, comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus, or a taxi. The measurements are carried out by embedded sensor accelerometers in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they perform the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertically stored in an Excel Macro-enabled Workbook (xlsm) format that can be used to train an AI model in a smartphone which has the potential to collect people’s vibration data and decide what movement is being conducted. Moreover, with more data received, the database can be updated and used to train the model with a larger dataset. The prevalence of the smartphone opens the door to crowdsensing, which leads to the pattern of people taking public transport being understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transport, services and schedules can be planned perceptively.https://www.mdpi.com/2306-5729/6/10/104human activitysmartphoneaccelerometer |
| spellingShingle | Sakdirat Kaewunruen Jessada Sresakoolchai Junhui Huang Satoru Harada Wisinee Wisetjindawat Human Activity Vibrations Data human activity smartphone accelerometer |
| title | Human Activity Vibrations |
| title_full | Human Activity Vibrations |
| title_fullStr | Human Activity Vibrations |
| title_full_unstemmed | Human Activity Vibrations |
| title_short | Human Activity Vibrations |
| title_sort | human activity vibrations |
| topic | human activity smartphone accelerometer |
| url | https://www.mdpi.com/2306-5729/6/10/104 |
| work_keys_str_mv | AT sakdiratkaewunruen humanactivityvibrations AT jessadasresakoolchai humanactivityvibrations AT junhuihuang humanactivityvibrations AT satoruharada humanactivityvibrations AT wisineewisetjindawat humanactivityvibrations |