short-paper

Data set for fall events and daily activities from inertial sensors

Authors:

Olukunle Ojetola,

James BruseyAuthors Info & Claims

MMSys '15: Proceedings of the 6th ACM Multimedia Systems Conference

Pages 243 - 248

https://doi.org/10.1145/2713168.2713198

Published: 18 March 2015 Publication History

Abstract

Wearable sensors are becoming popular for remote health monitoring as technology improves and cost reduces. One area in which wearable sensors are increasingly being used is falls monitoring. The elderly, in particular are vulnerable to falls and require continuous monitoring. Indeed, many attempts, with insufficient success have been made towards accurate, robust and generic falls and Activities of Daily Living (ADL) classification. A major challenge in developing solutions for fall detection is access to sufficiently large data sets.

This paper presents a description of the data set and the experimental protocols designed by the authors for the simulation of falls, near-falls and ADL. Forty-two volunteers were recruited to participate in an experiment that involved a set of scripted protocols. Four types of falls (forward, backward, lateral left and right) and several ADL were simulated. This data set is intended for the evaluation of fall detection algorithms by combining daily activities and transitions from one posture to another with falls. In our prior work, machine learning based fall detection algorithms were developed and evaluated. Results showed that our algorithm was able to discriminate between falls and ADL with an F-measure of 94%.

References

[1]

Arvind, D., and Valtazanos, A. Speckled tango dancers: Real-time motion capture of two-body interactions using on-body wireless sensor networks. In Wearable and Implantable Body Sensor Networks, 2009. BSN 2009. Sixth International Workshop on (June 2009), pp. 312--317.

Digital Library

[2]

Bannach, D., Amft, O., Kunze, K., Heinz, E., Troster, G., and Lukowicz, P. Waving real hand gestures recorded by wearable motion sensors to a virtual car and driver in a mixed-reality parking game. In Computational Intel ligence and Games, 2007. CIG 2007. IEEE Symposium on (April 2007), pp. 32--39.

[3]

Bekibele, C. O., and Gureje, O. Fall incidence in a population of elderly persons in nigeria. Gerontology 56(3) (2010), 278--283.

[4]

Bloch, F., Gautier, V., Noury, N., Lundy, J. E., Poujaud, J., Claessens, Y. E., and Rigaud, A. S. Evaluation under real-life conditions of a stand-alone fall detector for the elderly subjects. Annals of Physical and Rehabilitation Medicine 54, 6 (2011), 391--398.

[5]

Bourke, A. K., O'Brien, J. V., and Lyons, G. M. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & Posture 26, 2 (2007), 194--199.

[6]

Braga, F., Forlani, C., and Signorini, M. A knowledge based home monitoring system for management and rehabilitation of cardiovascular patients. In Computers in Cardiology, 2005 (Sept 2005), pp. 41--44.

[7]

Burns, A., Greene, B., McGrath, M., O'Shea, T., Kuris, B., Ayer, S., Stroiescu, F., and Cionca, V. Shimmer - a wireless sensor platform for noninvasive biomedical research. Sensors Journal, IEEE 10, 9 (2010), 1527--1534.

[8]

Chen, J., Karric, K., Chang, D., Luk, J., and Bajcsy, R. Wearable sensors for reliable fall detection. In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the (January 2005), pp. 3551--3554.

[9]

Dai, J., Bai, X., Yang, Z., Shen, Z., and Xuan, D. PerFallD: A pervasive fall detection system using mobile phones. In Proc. 8th Intl. Conf. Pervasive Computing and Communications Workshops (PERCOM Workshops) (March 2010), pp. 292--297.

[10]

Doughty, K., Lewis, R., and McIntosh, A. The design of a practical and reliable fall detector for community and institutional telecare. Journal of Telemedicine and Telecare 6, 1 (2000), 150--154.

[11]

Fitzgerald, D., Foody, J., Kelly, D., Ward, T., Markham, C., McDonald, J., and Caulfield, B. Development of a wearable motion capture suit and virtual reality biofeedback system for the instruction and analysis of sports rehabilitation exercises. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (Aug 2007), pp. 4870--4874.

[12]

Jitapunkul, S., Songkhla, M. N., Chayovan, N., Chirawatkul, A., Choprapawon, C., Kachondham, Y., and Buasai, S. Falls and their associated factors: a national survey of the thai elderly. J Med Assoc Thai 81, 4 (1998), 233--242.

[13]

Li, Q., Stankovic, J. A., Hanson, M. A., Barth, A. T., Lach, J., and Zhou, G. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In BSN '09: Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks (2009), IEEE Computer Society, pp. 138--143.

Digital Library

[14]

Liu, S., and Cheng, W. Fall detection with the support vector machine during scripted and continuous unscripted activities. Sensors 12, 9 (2012), 12301--12316.

[15]

Liu, T., Inoue, Y., Shibata, K., and Morioka, H. Development of wearable sensor combinations for human lower extremity motion analysis. In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on (May 2006), pp. 1655--1660.

[16]

Lord, C. J., and Colvin, D. P. Falls in the elderly: Detection and assessment. In Proc. Ann. Intl. Conf. Engineering in Medicine and Biology Society (EMBS '91) (October 1991), pp. 1938--1939.

[17]

Nakamura, M., Nakamura, J., Shuzo, M., Warisawa, S., and Yamada, I. Collaborative processing of wearable and ambient sensor system for health monitoring application. In Applied Sciences in Biomedical and Communication Technologies (ISABEL), 2010 3rd International Symposium on (Nov 2010), pp. 1--5.

[18]

Ojetola, O. Detection of Human Fal ls using Wearable Sensors. PhD thesis, Coventry University, Coventry UK, 2014.

[19]

Rodgers, M., Pai, V., andConroy, R. Recent advances in wearable sensors for health monitoring. Sensors Journal, IEEE PP, 99 (2014), 1--1.

[20]

Roys, M. S. Serious stair injuries can be prevented by improved stair design. Applied Ergonomics 32, 2 (2001), 135--139.

[21]

Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., andBowyer, K. The humanid gait challenge problem: data sets, performance, and analysis. Pattern Analysis and Machine Intel ligence, IEEE Transactions on 27, 2 (Feb 2005), 162--177.

Digital Library

[22]

Stevens, J. A., and Olson, S. Reducing falls and resulting hip fractures among older women. Home care provider 5, 4 (2000), 134--141.

[23]

Tolkiehn, M., Atallah, L., Lo, B., and Yang, G. Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE (September 2011), pp. 369--372.

[24]

Trafton, A. Balance problems? step into the ishoe. {online} MIT news. http://web. mit.edu/newsoffice/2008/i-shoe-0716., (2009). {Accessed 06-January-2015}.

[25]

Tynetec. Wrist Worn Fall Detector. {online} http://www.tynetec.co.uk/telecare-devices/wrist-worn-fall-detector. {Accessed 06-January-2015}.

[26]

Vavoulas, G., Pediaditis, M., Spanakis, E., and Tsiknakis, M. The mobifall dataset: An initial evaluation of fall detection algorithms using smartphones. In Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on (Nov 2013), pp. 1--4.

[27]

Vivatec. WristCare Social Services Range. {online} http://www.obsmedical.com/assets/uploads/wp-uploads/2010/03/WristCare-Social-Services-v2-Range.pdf, (2007). {Accessed 06-January-2015}.

[28]

Xsens. Customer cases for Film. {online} https://www.xsens.com/customer-cases/?app=Film, (2014). {Accessed 06-January-2015}.

[29]

Yang, C.-C., and Hsu, Y.-L. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10, 8 (2010), 7772--7788.

[30]

Zhang, J. T., Novak, A. C., Brouwer, B., and Li, Q. Concurrent validation of xsens mvn measurement of lower limb joint angular kinematics. Physiological Measurement 34, 8 (2013), N63.

[31]

Zhang, T., Wang, J., Xu, L., and Liu, P. Fall detection by wearable sensor and one-class svm algorithm. In Intel ligent Computing in Signal Processing and Pattern Recognition, vol. 345. 2006, pp. 858--863.

Cited By

Rassekh ESnidaro L(2025)Survey on data fusion approaches for fall-detectionInformation Fusion10.1016/j.inffus.2024.102696114(102696)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102696
Tang JHe BXu JTan TWang ZZhou YJiang S(2024)Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor ConfigurationIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.337039632(1233-1245)Online publication date: 2024
https://doi.org/10.1109/TNSRE.2024.3370396
Kaur RSharma RDhaliwal M(2024)Physiological and Inertial Features based Dataset for Falls and Activities: PIF v2Procedia Computer Science10.1016/j.procs.2024.04.120235(1268-1277)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.04.120
Show More Cited By

Index Terms

Data set for fall events and daily activities from inertial sensors
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Document collection models
  2. Information systems applications
    1. Digital libraries and archives

Recommendations

Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

Falling is a serious threat to the elderly people. One severe fall can cause hazardous problems like bone fracture or may lead to some permanent disability or even death. Thus, it has become the need of the hour to continuously monitor the activities of ...
A smartphone-based fall detection system

Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a ...
Wearable sensors for recognizing individuals undertaking daily activities
ISWC '18: Proceedings of the 2018 ACM International Symposium on Wearable Computers

With the fast evolution in the area of processing units and sensors, wearable devices are becoming more popular among people of all ages. Recently, there has been renewed interest in exploiting the capabilities of wearable sensors for person recognition ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MMSys '15: Proceedings of the 6th ACM Multimedia Systems Conference

March 2015

277 pages

ISBN:9781450333511

DOI:10.1145/2713168

General Chair:
Wei Tsang Ooi
National University of Singapore, Singapore
,
Program Chairs:
Wu-chi Feng
Portland State University
,
Feng Liu
Portland State University

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

MMSys '15

Sponsor:

MMSys '15: Multimedia Systems Conference 2015

March 18 - 20, 2015

Oregon, Portland

Acceptance Rates

MMSys '15 Paper Acceptance Rate 12 of 41 submissions, 29%;

Overall Acceptance Rate 176 of 530 submissions, 33%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

69
Total Citations
View Citations
663
Total Downloads

Downloads (Last 12 months)46
Downloads (Last 6 weeks)7

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Rassekh ESnidaro L(2025)Survey on data fusion approaches for fall-detectionInformation Fusion10.1016/j.inffus.2024.102696114(102696)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102696
Tang JHe BXu JTan TWang ZZhou YJiang S(2024)Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor ConfigurationIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.337039632(1233-1245)Online publication date: 2024
https://doi.org/10.1109/TNSRE.2024.3370396
Kaur RSharma RDhaliwal M(2024)Physiological and Inertial Features based Dataset for Falls and Activities: PIF v2Procedia Computer Science10.1016/j.procs.2024.04.120235(1268-1277)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.04.120
Jarrah MToubal ABengherbia B(2024)Enhancing Elderly Care with Wearable Technology: Development of a Dataset for Fall Detection and ADL Classification During Muslim Prayer ActivitiesArabian Journal for Science and Engineering10.1007/s13369-024-09478-5Online publication date: 18-Sep-2024
https://doi.org/10.1007/s13369-024-09478-5
Kaur RSharma R(2024)Wearable sensors and datasets for evaluating systems predicting falls and activities of daily living: recent advances and methodologyMultimedia Tools and Applications10.1007/s11042-024-19504-1Online publication date: 6-Jun-2024
https://doi.org/10.1007/s11042-024-19504-1
Dhaliwal MSharma RKaur R(2024)PIF dataset: a comprehensive dataset of physiological and inertial features for recognition of human activitiesMultimedia Tools and Applications10.1007/s11042-024-19285-7Online publication date: 2-May-2024
https://doi.org/10.1007/s11042-024-19285-7
Moutsis STsintotas KGasteratos A(2023)PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis AccelerometersSensors10.3390/s2318795123:18(7951)Online publication date: 18-Sep-2023
https://doi.org/10.3390/s23187951
Chan HOuyang YChen RLai YKuo CLiao GHsu WChang Y(2023)Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial SensingSensors10.3390/s2301049523:1(495)Online publication date: 2-Jan-2023
https://doi.org/10.3390/s23010495
Huang MGarcia A(2023)Dataset and System Design for Orthopedic Walker Fall Detection and Activity Logging Using Motion ClassificationApplied Sciences10.3390/app13201137913:20(11379)Online publication date: 17-Oct-2023
https://doi.org/10.3390/app132011379
Zhang MGong D(2023)Fall Detection for the Elderly Based on Online Transfer Learning2023 42nd Chinese Control Conference (CCC)10.23919/CCC58697.2023.10240562(4340-4345)Online publication date: 24-Jul-2023
https://doi.org/10.23919/CCC58697.2023.10240562
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents