Abstract
Mobility deteriorates with age, and falls become more frequent, resulting in injuries or even death. However, many such injuries and deaths can be prevented, resulting in financial savings. This paper proposes a method to identify and distinguish falls from activities of daily living (ADLs) in older adults utilizing a wearable fall detection device. Novel preprocessing and feature extraction techniques to extract features from accelerometry data were developed. In addition, machine-learning techniques, such as support vector machine (SVM), k-nearest neighbor (k-NN), random forest (RF), and artificial neural network (ANN), were used to classify the acceleration and rotation signals into falls or ADLs. The main contributions of the research are the implementation of a two-class classification algorithm for fall detection and analyzing the effect of sliding window size on system performance. The publicly available SisFall dataset was utilized to develop the fall detection algorithm, and various window sizes were evaluated. The results show that the best compromise between processing time and detection performance is achieved with a window size of 3 s. The proposed and implemented approaches employing the SVM algorithm demonstrated a perfect F-1 score and recall value of 100% when testing for a fall. We achieved an accuracy of 96.34% by using the k-NN algorithm. Furthermore, the ensemble machine-learning algorithms, SVM and RF, achieved accuracy, sensitivity, and specificity higher than 99%.
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References
Centers for Disease Control and Prevention (n.d.) Deaths from older adult falls. https://www.cdc.gov/falls/data/fall-deaths.html. Accessed 20 July 2022
Centers for Disease Control and Prevention (n.d.) Keep on your feet - preventing older adult falls. https://www.cdc.gov/injury/features/older-adult-falls/index.html. Accessed 20 July 2022
Deandrea S et al (2010) Risk factors for falls in community-dwelling older people: a systematic review and meta-analysis. Epidemiology 21(5):658–668
Waheed M et al (2021) NT-FDS—a noise tolerant fall detection system using deep learning on wearable devices. Sensors 21(6). https://doi.org/10.3390/s21062006
Costa A et al (2012) Sensor-driven agenda for intelligent home care of the elderly. Expert Syst Appl 39(15):12192–12204
Santhosh SR (2021) Healthcare monitoring system for elderly or disabled persons using IoT. Test Eng Manage 83:5005–5008
Foroughi H et al (2008) Intelligent video surveillance for monitoring fall detection of elderly in home environments. 11th International Conference on Computer and Information Technology, pp 219–224
Muheidat F, Tawalbeh AL (2020) In-home floor based sensor system-smart carpet- to facilitate healthy aging in place (AIP). IEEE Access 8:178627–178638
Daher M et al (2017) Elder tracking and fall detection system using smart tiles. IEEE Sens J 17(2):469–479
Mokhtari G et al (2018) Fall detection in smart home environments using UWB sensors and unsupervised change detection. J Reliable Intell Environ 4:131–139
Saleh M, Jeannès RLB (2019) Elderly fall detection using wearable sensors: a low cost highly accurate algorithm. IEEE Sens J 19(8):3156–3164
Xu T et al (2021) A two-step fall detection algorithm combining threshold-based method and convolutional neural network. Metrol Meas Syst 28(1):23–40
Plamerini L et al (2020) Accelerometer-based fall detection using machine learning: training and testing on real-world falls. Sensors 20(22):6479. https://doi.org/10.3390/s20226479
Liu SH, Cheng WC (2012) Fall detection with the support vector machine during scripted and continuous unscripted activities. Sensors 12(9):12301–12316
Casilari E et al (2020) A study of the use of gyroscope measurements in wearable fall detection systems. Symmetry 12(4):649. https://doi.org/10.3390/sym12040649
Radmanesh E et al (2020) A wearable IoT-based fall detection system using triaxial accelerometer and barometric pressure sensor. In: Lecture Notes in Computer Science, vol. 12293, Springer. https://doi.org/10.1007/978-3-030-58008-7_13
Tolkiehn M et al (2011) Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 369–372. https://doi.org/10.1109/IEMBS.2011.6090120
Grisales-Franco FM et al (2015) Fall detection algorithm based on thresholds and residual events. In: Pardo A, Kittler J (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, vol 9423. Springer. https://doi.org/10.1007/978-3-319-25751-8_69
Fudickar S et al (2014) Threshold-based fall detection on smart phones. In: Proceedings of the International Conference on Health Informatics (HEALTHINF-2014), pp 303–309
He J et al (2019) A low power fall sensing technology based on FD-CNN. IEEE Sens J 19(13):5110–5118
Aziz O et al (2016) A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med Biol Eng Compu 55(1):45–55
Giuffrida D et al (2019) Fall detection with supervised machine learning using wearable sensors. In: IEEE 17th International Conference on Industrial Informatics
Vallabh P et al (2016) Fall detection using machine learning algorithms. In: 24th International Conference on Software, Telecommunications and Computer Networks
Boulellaa E et al (2019) Covariance matrix based fall detection from multiple wearable sensors. J Biomed Inform 94. https://doi.org/10.1016/j.jbi.2019.103189
Wisesa IWW, Mahardika G (2019) Fall detection algorithm based on accelerometer and gyroscope sensor data using Recurrent Neural Networks. IOP Conf Ser: Earth Environ Sci 258:012035
Al Nahian J et al (2021) Towards an accelerometer-based elderly fall detection system using cross-disciplinary time series features. IEEE Access 9:39413–39431
Rodrigues TB et al (2018) Fall detection system by machine learning framework for public health. Procedia Comput Sci 141:358–365
Soni M et al (2020) An Approach To Enhance Fall Detection Using Machine Learning Classifier. In the 12th International Conference on Computational Intelligence and Communication Networks
Yang X et al (2010) A wearable real-time fall detector based on Naive Bayes classifier. CCECE 2010:1–4. https://doi.org/10.1109/CCECE.2010.5575129
Özdemir AT (2016) An analysis on sensor locations of the human body for wearable fall detection devices: principles and practice. Sensors 16(8):1161. https://doi.org/10.3390/s16081161
Kubat M (2021) An introduction to machine learning, 3rd edn. Springer Nature Switzerland
Witten I et al (2017) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Cambridge
Casilari E et al (2017) Analysis of public datasets for wearable fall detection systems. Sensors 17(7):1513. https://doi.org/10.3390/s17071513
Sucerquia A et al (2017) SisFall: A fall and movement dataset. Sensors 17(1):198. https://doi.org/10.3390/s17010198
Johansson V (n.d.) A Sensor Orientation and Signal Preprocessing Study of A Person Fall Detection Algorithms, BSc thesis, Faculty of Natural
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Kausar, F., Mesbah, M., Iqbal, W. et al. Fall Detection in the Elderly using Different Machine Learning Algorithms with Optimal Window Size. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02215-6
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DOI: https://doi.org/10.1007/s11036-023-02215-6