The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors
Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-cons...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/7/1698 |
| _version_ | 1828739699008602112 |
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| author | Heesu Park Suh-Yeon Dong Miran Lee Inchan Youn |
| author_facet | Heesu Park Suh-Yeon Dong Miran Lee Inchan Youn |
| author_sort | Heesu Park |
| collection | DOAJ |
| description | Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system. |
| first_indexed | 2024-04-13T00:26:57Z |
| format | Article |
| id | doaj.art-ac626446910a44569c590a3b51964546 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:26:57Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ac626446910a44569c590a3b519645462022-12-22T03:10:35ZengMDPI AGSensors1424-82202017-07-01177169810.3390/s17071698s17071698The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable SensorsHeesu Park0Suh-Yeon Dong1Miran Lee2Inchan Youn3Center for Bionics, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaCenter for Bionics, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaCenter for Bionics, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaCenter for Bionics, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaHuman-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system.https://www.mdpi.com/1424-8220/17/7/1698HRV parametersactivity recognitionenergy expenditure estimationwearable sensorsmobile healthcare system |
| spellingShingle | Heesu Park Suh-Yeon Dong Miran Lee Inchan Youn The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors Sensors HRV parameters activity recognition energy expenditure estimation wearable sensors mobile healthcare system |
| title | The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors |
| title_full | The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors |
| title_fullStr | The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors |
| title_full_unstemmed | The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors |
| title_short | The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors |
| title_sort | role of heart rate variability parameters in activity recognition and energy expenditure estimation using wearable sensors |
| topic | HRV parameters activity recognition energy expenditure estimation wearable sensors mobile healthcare system |
| url | https://www.mdpi.com/1424-8220/17/7/1698 |
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