Research Article
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Enhancing Fall Detection Accuracy: The Ground-Face Coordinate System for 3D Accelerometer Data

Year 2024, Volume: 7 Issue: 3, 439 - 448, 31.12.2024

Abdullah Talha Sözer

https://doi.org/10.35377/saucis...1522290

Abstract

The global elderly population is on the rise, leading to increased physical, sensory, and cognitive changes that heighten the risk of falls. Consequently, fall detection (FD) has emerged as a significant concern, attracting considerable attention in recent years. Utilizing 3D accelerometer sensors for FD offers advantages such as cost-effectiveness and ease of implementation; however, traditional raw 3D accelerometer signals are inherently dependent on the device's orientation and placement within the device coordinate system. Misalignment between the device's axes and the direction of movement can lead to misinterpretation of acceleration signals, potentially causing misclassification of activities and resulting in false positives or missed falls. This study introduces a novel coordinate system called "ground-face," which is designed to be independent of the device's orientation and placement. In this system, the vertical axis is aligned perpendicularly to the Earth, while the device's x-axis is aligned with the individual's direction of movement. To assess the potential of the vertical component of ground-face referenced accelerometer signals for FD, it was compared with the commonly used acceleration magnitude signal. Detailed analysis was conducted using frequently preferred features in FD studies, and fall detection was performed with various classifiers. Comprehensive experiments demonstrated that the vertical component of the ground-face signal effectively characterizes falls, yielding approximately a 2% improvement in detection accuracy. Moreover, the proposed coordinate system is not limited to FD but can also be applied to human activity recognition (HAR) systems. By mitigating orientation-related discrepancies, it reduces the likelihood of misclassification and enhances the overall HAR capabilities.

Keywords

Fall detection Accelerometer Device coordinate system Global coordinate system Movement direction

Ethical Statement

This study does not require ethics committee permission or any special permission

Supporting Institution

The author has no received any financial support for the research, authorship or publication of this study.

References

  • World Health Organization, “Step safely: Strategies for preventing and managing falls across the life-course,” GENEVA.
  • R. Rajagopalan, I. Litvan, and T. P. Jung, “Fall prediction and prevention systems: Recent trends, challenges, and future research directions,” Sensors (Switzerland), vol. 17, no. 11, pp. 1–17, 2017.
  • United Nations, “World Population Ageing 2019,” New York.
  • K. C. Liu, C. Y. Hsieh, H. Y. Huang, S. J. P. Hsu, and C. T. Chan, “An Analysis of Segmentation Approaches and Window Sizes in Wearable-Based Critical Fall Detection Systems with Machine Learning Models,” IEEE Sens. J., vol. 20, no. 6, pp. 3303–3313, 2020.
  • J. Marques and P. Moreno, “Online Fall Detection Using Wrist Devices,” Sensors, vol. 23, no. 3, 2023.
  • T. Huang, M. Li, and J. Huang, “Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson’s disease: A systematic review,” Front. Aging Neurosci., vol. 15, 2023.
  • E. A. De La Cal, M. Fáñez, M. Villar, J. R. Villar, and V. M. González, “A low-power HAR method for fall and high-intensity ADLs identification using wrist-worn accelerometer devices,” Log. J. IGPL, vol. 31, no. 2, pp. 375–389, 2023.
  • P. Bhattacharjee, S. Biswas, S. Chattopadhyay, S. Roy, and S. Chakraborty, “Smart Assistance to Reduce the Fear of Falling in Parkinson Patients Using IoT,” Wirel. Pers. Commun., vol. 130, no. 1, pp. 281–302, 2023.
  • H.-C. Lin, M.-J. Chen, C.-H. Lee, L.-C. Kung, and J.-T. Huang, “Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT,” Sensors, vol. 23, no. 12, 2023.
  • A. Alqahtani, S. Alsubai, M. Sha, V. Peter, A. S. Almadhor, and S. Abbas, “Falling and Drowning Detection Framework Using Smartphone Sensors,” Comput. Intell. Neurosci., vol. 2022, 2022.
  • B. Brew, S. G. Faux, and E. Blanchard, “Effectiveness of a Smartwatch App in Detecting Induced Falls: Observational Study,” JMIR Form. Res., vol. 6, no. 3, 2022.
  • M. E. Issa, A. M. Helm, M. A. A. Al-Qaness, A. Dahou, M. A. Elaziz, and R. Damaševičius, “Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things,” Healthc., vol. 10, no. 6, 2022.
  • A. N. Pereira et al., “Flexible Sensor Suite Integrated into Textile for Calcium Ion and Fall Detection,” IEEE Sensors Lett., vol. 6, no. 10, 2022.
  • Y. H. Nho, J. G. Lim, and D. S. Kwon, “Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device,” IEEE Access, vol. 8, pp. 40389–40401, 2020.
  • M. Saleh and R. L. B. Jeannes, “Elderly Fall Detection Using Wearable Sensors: A Low Cost Highly Accurate Algorithm,” IEEE Sens. J., vol. 19, no. 8, pp. 3156–3164, 2019.
  • C. Wang et al., “Low-Power Fall Detector Using Triaxial Accelerometry and Barometric Pressure Sensing,” IEEE Trans. Ind. Informatics, vol. 12, no. 6, pp. 2302–2311, 2016.
  • X. Wang, J. Ellul, and G. Azzopardi, “Elderly Fall Detection Systems: A Literature Survey,” Front. Robot. AI, vol. 7, no. June, 2020.
  • J. L. Chua, Y. C. Chang, and W. K. Lim, “A simple vision-based fall detection technique for indoor video surveillance,” Signal, Image Video Process., vol. 9, no. 3, pp. 623–633, 2015.
  • M. G. Amin, Y. D. Zhang, F. Ahmad, and K. C. D. Ho, “Radar signal processing for elderly fall detection: The future for in-home monitoring,” IEEE Signal Process. Mag., vol. 33, no. 2, pp. 71–80, 2016.
  • B. Kwolek and M. Kepski, “Human fall detection on embedded platform using depth maps and wireless accelerometer,” Comput. Methods Programs Biomed., vol. 117, no. 3, pp. 489–501, 2014.
  • Y. Wang, K. Wu, and L. M. Ni, “WiFall: Device-Free Fall Detection by Wireless Networks,” IEEE Trans. Mob. Comput., vol. 16, no. 2, pp. 581–594, 2017.
  • S. K. Gharghan and H. A. Hashim, “A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques,” Measurement, vol. 226, p. 114186, Feb. 2024.
  • P. Kumar, S. Chauhan, and L. K. Awasthi, “Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions,” Arch. Comput. Methods Eng., Aug. 2023.
  • L. Minh Dang, K. Min, H. Wang, M. Jalil Piran, C. Hee Lee, and H. Moon, “Sensor-based and vision-based human activity recognition: A comprehensive survey,” Pattern Recognit., vol. 108, p. 107561, Dec. 2020.
  • L. Palmerini, J. Klenk, C. Becker, and L. Chiari, “Accelerometer-based fall detection using machine learning: Training and testing on real-world falls,” Sensors (Switzerland), vol. 20, no. 22, pp. 1–15, 2020.
  • N. Pannurat, S. Thiemjarus, and E. Nantajeewarawat, “Automatic fall monitoring: A review,” Sensors (Switzerland), vol. 14, no. 7, pp. 12900–12936, 2014.
  • S. Nooruddin, M. M. Islam, F. A. Sharna, H. Alhetari, and M. N. Kabir, “Sensor-based fall detection systems: A review,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 5, pp. 2735–2751, 2022.
  • J. A. Santoyo-Ramón, E. Casilari, and J. M. Cano-García, “A study of one-class classification algorithms for wearable fall sensors,” Biosensors, vol. 11, no. 8, pp. 1–20, 2021.
  • H. Kimura, M. Nakamura, N. Inou, M. Matsudaira, and M. Yoshida, “Identification Method of Sensor Directions and Sensitivities in Multi-Axis Accelerometer (Actual Measurement of Direction Tensor and Sensitivity Tensor),” J. Robot. Mechatronics, vol. 25, no. 2, pp. 408–416, Apr. 2013.
  • J. R. Villar, C. Chira, E. de la Cal, V. M. González, J. Sedano, and S. B. Khojasteh, “Autonomous on-wrist acceleration-based fall detection systems: Unsolved challenges,” Neurocomputing, vol. 452, pp. 404–413, 2021.
  • M. Abbas and R. L. B. Jeannes, “Exploiting Local Temporal Characteristics via Multinomial Decomposition Algorithm for Real-Time Activity Recognition,” IEEE Trans. Instrum. Meas., vol. 70, 2021.
  • G. Šeketa, L. Pavlaković, D. Džaja, I. Lacković, and R. Magjarević, “Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms,” Sensors, vol. 21, no. 13, p. 4335, Jun. 2021.
  • C. Mosquera-Lopez et al., “Automated Detection of Real-World Falls: Modeled from People with Multiple Sclerosis,” IEEE J. Biomed. Heal. Informatics, vol. 25, no. 6, pp. 1975–1984, 2021.
  • M. J. Al Nahian et al., “Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features,” IEEE Access, vol. 9, pp. 39413–39431, 2021.
  • M. Saleh, M. Abbas, and R. B. Le Jeannes, “FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications,” IEEE Sens. J., vol. 21, no. 2, pp. 1849–1858, 2021.
  • Y. Yan and Y. Ou, “Accurate fall detection by nine-axis IMU sensor,” 2017 IEEE Int. Conf. Robot. Biomimetics, ROBIO 2017, vol. 2018-Janua, pp. 1–6, 2018.
  • M. M. Musngi, O. Aziz, S. Zihajehzadeh, G. C. Nazareth, C. G. Tae, and E. J. Park, “Use of Average Vertical Velocity and Difference in Altitude for Improving Automatic Fall Detection from Trunk Based Inertial and Barometric Pressure Measurements,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2018-July, pp. 5146–5149, 2018.
  • J. K. Lee, S. N. Robinovitch, and E. J. Park, “Inertial Sensing-Based Pre-Impact Detection of Falls Involving Near-Fall Scenarios,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 23, no. 2, pp. 258–266, 2015.
  • A. Ferreira, G. Santos, A. Rocha, and S. Goldenstein, “User-Centric Coordinates for Applications Leveraging 3-Axis Accelerometer Data,” IEEE Sens. J., vol. 17, no. 16, pp. 5231–5243, 2017.
  • X. Yu, J. Jang, and S. Xiong, “A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors,” Front. Aging Neurosci., vol. 13, Jul. 2021.

Year 2024, Volume: 7 Issue: 3, 439 - 448, 31.12.2024

Abdullah Talha Sözer

https://doi.org/10.35377/saucis...1522290

Abstract

References

  • World Health Organization, “Step safely: Strategies for preventing and managing falls across the life-course,” GENEVA.
  • R. Rajagopalan, I. Litvan, and T. P. Jung, “Fall prediction and prevention systems: Recent trends, challenges, and future research directions,” Sensors (Switzerland), vol. 17, no. 11, pp. 1–17, 2017.
  • United Nations, “World Population Ageing 2019,” New York.
  • K. C. Liu, C. Y. Hsieh, H. Y. Huang, S. J. P. Hsu, and C. T. Chan, “An Analysis of Segmentation Approaches and Window Sizes in Wearable-Based Critical Fall Detection Systems with Machine Learning Models,” IEEE Sens. J., vol. 20, no. 6, pp. 3303–3313, 2020.
  • J. Marques and P. Moreno, “Online Fall Detection Using Wrist Devices,” Sensors, vol. 23, no. 3, 2023.
  • T. Huang, M. Li, and J. Huang, “Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson’s disease: A systematic review,” Front. Aging Neurosci., vol. 15, 2023.
  • E. A. De La Cal, M. Fáñez, M. Villar, J. R. Villar, and V. M. González, “A low-power HAR method for fall and high-intensity ADLs identification using wrist-worn accelerometer devices,” Log. J. IGPL, vol. 31, no. 2, pp. 375–389, 2023.
  • P. Bhattacharjee, S. Biswas, S. Chattopadhyay, S. Roy, and S. Chakraborty, “Smart Assistance to Reduce the Fear of Falling in Parkinson Patients Using IoT,” Wirel. Pers. Commun., vol. 130, no. 1, pp. 281–302, 2023.
  • H.-C. Lin, M.-J. Chen, C.-H. Lee, L.-C. Kung, and J.-T. Huang, “Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT,” Sensors, vol. 23, no. 12, 2023.
  • A. Alqahtani, S. Alsubai, M. Sha, V. Peter, A. S. Almadhor, and S. Abbas, “Falling and Drowning Detection Framework Using Smartphone Sensors,” Comput. Intell. Neurosci., vol. 2022, 2022.
  • B. Brew, S. G. Faux, and E. Blanchard, “Effectiveness of a Smartwatch App in Detecting Induced Falls: Observational Study,” JMIR Form. Res., vol. 6, no. 3, 2022.
  • M. E. Issa, A. M. Helm, M. A. A. Al-Qaness, A. Dahou, M. A. Elaziz, and R. Damaševičius, “Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things,” Healthc., vol. 10, no. 6, 2022.
  • A. N. Pereira et al., “Flexible Sensor Suite Integrated into Textile for Calcium Ion and Fall Detection,” IEEE Sensors Lett., vol. 6, no. 10, 2022.
  • Y. H. Nho, J. G. Lim, and D. S. Kwon, “Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device,” IEEE Access, vol. 8, pp. 40389–40401, 2020.
  • M. Saleh and R. L. B. Jeannes, “Elderly Fall Detection Using Wearable Sensors: A Low Cost Highly Accurate Algorithm,” IEEE Sens. J., vol. 19, no. 8, pp. 3156–3164, 2019.
  • C. Wang et al., “Low-Power Fall Detector Using Triaxial Accelerometry and Barometric Pressure Sensing,” IEEE Trans. Ind. Informatics, vol. 12, no. 6, pp. 2302–2311, 2016.
  • X. Wang, J. Ellul, and G. Azzopardi, “Elderly Fall Detection Systems: A Literature Survey,” Front. Robot. AI, vol. 7, no. June, 2020.
  • J. L. Chua, Y. C. Chang, and W. K. Lim, “A simple vision-based fall detection technique for indoor video surveillance,” Signal, Image Video Process., vol. 9, no. 3, pp. 623–633, 2015.
  • M. G. Amin, Y. D. Zhang, F. Ahmad, and K. C. D. Ho, “Radar signal processing for elderly fall detection: The future for in-home monitoring,” IEEE Signal Process. Mag., vol. 33, no. 2, pp. 71–80, 2016.
  • B. Kwolek and M. Kepski, “Human fall detection on embedded platform using depth maps and wireless accelerometer,” Comput. Methods Programs Biomed., vol. 117, no. 3, pp. 489–501, 2014.
  • Y. Wang, K. Wu, and L. M. Ni, “WiFall: Device-Free Fall Detection by Wireless Networks,” IEEE Trans. Mob. Comput., vol. 16, no. 2, pp. 581–594, 2017.
  • S. K. Gharghan and H. A. Hashim, “A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques,” Measurement, vol. 226, p. 114186, Feb. 2024.
  • P. Kumar, S. Chauhan, and L. K. Awasthi, “Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions,” Arch. Comput. Methods Eng., Aug. 2023.
  • L. Minh Dang, K. Min, H. Wang, M. Jalil Piran, C. Hee Lee, and H. Moon, “Sensor-based and vision-based human activity recognition: A comprehensive survey,” Pattern Recognit., vol. 108, p. 107561, Dec. 2020.
  • L. Palmerini, J. Klenk, C. Becker, and L. Chiari, “Accelerometer-based fall detection using machine learning: Training and testing on real-world falls,” Sensors (Switzerland), vol. 20, no. 22, pp. 1–15, 2020.
  • N. Pannurat, S. Thiemjarus, and E. Nantajeewarawat, “Automatic fall monitoring: A review,” Sensors (Switzerland), vol. 14, no. 7, pp. 12900–12936, 2014.
  • S. Nooruddin, M. M. Islam, F. A. Sharna, H. Alhetari, and M. N. Kabir, “Sensor-based fall detection systems: A review,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 5, pp. 2735–2751, 2022.
  • J. A. Santoyo-Ramón, E. Casilari, and J. M. Cano-García, “A study of one-class classification algorithms for wearable fall sensors,” Biosensors, vol. 11, no. 8, pp. 1–20, 2021.
  • H. Kimura, M. Nakamura, N. Inou, M. Matsudaira, and M. Yoshida, “Identification Method of Sensor Directions and Sensitivities in Multi-Axis Accelerometer (Actual Measurement of Direction Tensor and Sensitivity Tensor),” J. Robot. Mechatronics, vol. 25, no. 2, pp. 408–416, Apr. 2013.
  • J. R. Villar, C. Chira, E. de la Cal, V. M. González, J. Sedano, and S. B. Khojasteh, “Autonomous on-wrist acceleration-based fall detection systems: Unsolved challenges,” Neurocomputing, vol. 452, pp. 404–413, 2021.
  • M. Abbas and R. L. B. Jeannes, “Exploiting Local Temporal Characteristics via Multinomial Decomposition Algorithm for Real-Time Activity Recognition,” IEEE Trans. Instrum. Meas., vol. 70, 2021.
  • G. Šeketa, L. Pavlaković, D. Džaja, I. Lacković, and R. Magjarević, “Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms,” Sensors, vol. 21, no. 13, p. 4335, Jun. 2021.
  • C. Mosquera-Lopez et al., “Automated Detection of Real-World Falls: Modeled from People with Multiple Sclerosis,” IEEE J. Biomed. Heal. Informatics, vol. 25, no. 6, pp. 1975–1984, 2021.
  • M. J. Al Nahian et al., “Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features,” IEEE Access, vol. 9, pp. 39413–39431, 2021.
  • M. Saleh, M. Abbas, and R. B. Le Jeannes, “FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications,” IEEE Sens. J., vol. 21, no. 2, pp. 1849–1858, 2021.
  • Y. Yan and Y. Ou, “Accurate fall detection by nine-axis IMU sensor,” 2017 IEEE Int. Conf. Robot. Biomimetics, ROBIO 2017, vol. 2018-Janua, pp. 1–6, 2018.
  • M. M. Musngi, O. Aziz, S. Zihajehzadeh, G. C. Nazareth, C. G. Tae, and E. J. Park, “Use of Average Vertical Velocity and Difference in Altitude for Improving Automatic Fall Detection from Trunk Based Inertial and Barometric Pressure Measurements,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2018-July, pp. 5146–5149, 2018.
  • J. K. Lee, S. N. Robinovitch, and E. J. Park, “Inertial Sensing-Based Pre-Impact Detection of Falls Involving Near-Fall Scenarios,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 23, no. 2, pp. 258–266, 2015.
  • A. Ferreira, G. Santos, A. Rocha, and S. Goldenstein, “User-Centric Coordinates for Applications Leveraging 3-Axis Accelerometer Data,” IEEE Sens. J., vol. 17, no. 16, pp. 5231–5243, 2017.
  • X. Yu, J. Jang, and S. Xiong, “A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors,” Front. Aging Neurosci., vol. 13, Jul. 2021.
There are 40 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Abdullah Talha Sözer 0000-0002-7855-6119

Early Pub Date December 24, 2024
Publication Date December 31, 2024
Submission Date July 25, 2024
Acceptance Date October 21, 2024
Published in Issue Year 2024Volume: 7 Issue: 3

Cite

IEEE A. T. Sözer, “Enhancing Fall Detection Accuracy: The Ground-Face Coordinate System for 3D Accelerometer Data”, SAUCIS, vol. 7, no. 3, pp. 439–448, 2024, doi: 10.35377/saucis...1522290.
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