Skip to main content Accessibility help


  • Ahsen Tahir (a1), Jawad Ahmad (a2), Gordon Morison (a2), Hadi Larijani (a2), Ryan M. Gibson (a2) and Dawn A. Skelton (a3)...


Falls are a major health concern in older adults. Falls lead to mortality, immobility and high costs to social and health care services. Early detection and classification of falls is imperative for timely and appropriate medical aid response. Traditional machine learning models have been explored for fall classification. While newly developed deep learning techniques have the ability to potentially extract high-level features from raw sensor data providing high accuracy and robustness to variations in sensor position, orientation and diversity of work environments that may skew traditional classification models. However, frequently used deep learning models like Convolutional Neural Networks (CNN) are computationally intensive. To the best of our knowledge, we present the first instance of a Hybrid Multichannel Random Neural Network (HMCRNN) architecture for fall detection and classification. The proposed architecture provides the highest accuracy of 92.23% with dropout regularization, compared to other deep learning implementations. The performance of the proposed technique is approximately comparable to a CNN yet requires only half the computation cost of the CNN-based implementation. Furthermore, the proposed HMCRNN architecture provides 34.12% improvement in accuracy on average than a Multilayer Perceptron.



Hide All
1.Basterrech, S., Mohammed, S., Rubino, G., & Soliman, M. (2009). Levenberg–marquardt training algorithms for random neural networks. The Computer Journal 54: 125135.
2.Boyle, T. & Ravenscroft, A. (2012). Context and deep learning design. Computers and Education 59: 12241233.
3.Brun, O., Yin, Y., & Gelenbe, E. (2018). Deep learning with dense random neural network for detecting attacks against IoT-connected home environments. Procedia Computer Science 134: 458463.
4.Chen, Y. & Xue, Y. (2015). A deep learning approach to human activity recognition based on single accelerometer. In 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, pp. 14881492.
5.Cleland, I., Kikhia, B., Nugent, C., Boytsov, A., Hallberg, J., Synnes, K., McClean, S., & Finlay, D. (2013). Optimal placement of accelerometers for the detection of everyday activities. Sensors 13: 91839200.
6.Consultants PCP market research (2012). Falls : measuring the impact on older people. and Reviews/Falls report_web_v2.pdf.
7.Coskun, D., Incel, O.D., & Ozgovde, A. (2015). Phone position/placement detection using accelerometer: Impact on activity recognition. In 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE, pp. 16.
8.Cramer, C.E. & Gelenbe, E. (2000). Video quality and traffic qos in learning-based subsampled and receiver-interpolated video sequences. IEEE Journal on Selected Areas in Communications 18: 150167.
9.Delaye, E., Sirasao, A., Dudha, C., & Das, S. (2017). Deep learning challenges and solutions with xilinx fpgas. In Proceedings of the 36th International Conference on Computer-Aided Design, IEEE, pp. 908913.
10.Gelenbe, E. (1989). Random neural networks with negative and positive signals and product form solution. Neural Computation 1: 502510.
11.Gelenbe, E. (1993). Learning in the recurrent random neural network. Neural Computation 5: 154164.
12.Gelenbe, E. & Hussain, K.F. (2002). Learning in the multiple class random neural network. IEEE Transactions on Neural Networks 13: 12571267.
13.Gelenbe, E. & Stafylopatis, A. (1991). Global behavior of homogeneous random neural systems. Applied Mathematical Modelling 15: 534541.
14.Gelenbe, E. & Yin, Y. (2016). Deep learning with random neural networks. In Proceedings of SAI Intelligent Systems Conference, Springer, pp. 450462.
15.Gelenbe, E. & Yin, Y. (2017). Deep learning with dense random neural networks. In International Conference on Man–Machine Interactions, Springer, pp. 318.
16.Gibson, R.M., Amira, A., Ramzan, N., Casaseca-de-la-Higuera, P., & Pervez, Z. (2016). Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic. Applied Soft Computing 39: 94103.
17.Gibson, R.M., Amira, A., Ramzan, N., Casaseca-de-la Higuera, P., & Pervez, Z. (2017). Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device. Biomedical Signal Processing and Control 33: 96108.
18.Grenet, I., Yin, Y., Comet, J.-P., & Gelenbe, E. (2018). Machine learning to predict toxicity of compounds. In International Conference on Artificial Neural Networks, Springer, Cham, pp. 335345.
19.Hinton, G. (2014). Lecture notes in neural networks for machine learning.
20.Khan, S.S. & Taati, B. (2017). Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders. Expert Systems with Applications 87: 280290.
21.Kwolek, B. & Kepski, M. (2014). Human fall detection on embedded platform using depth maps and wireless accelerometer. Computer Methods and Programs in Biomedicine 117: 489501.
22.Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A.T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine 48: 140150.
23.Lu, N., Ren, X., Song, J., & Wu, Y. (2017). Visual guided deep learning scheme for fall detection. In 13th IEEE Conference on Automation Science and Engineering (CASE), 2017, IEEE, pp. 801806.
24.Martins, A. & Astudillo, R. (2016). From softmax to sparsemax: A sparse model of attention and multi-label classification. In International Conference on Machine Learning, pp. 16141623.
25.Masud, T. & Morris, R.O. (2001). Epidemiology of Falls. Age and Ageing 30(4): 37.
26.Mathie, M.J., Coster, A.C., Lovell, N.H., & Celler, B.G. (2004). Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement 25: R1.
27.Nait Aicha, A., Englebienne, G., van Schooten, K.S., Pijnappels, M., & Kröse, B. (2018). Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry. Sensors 18: 114.
28.Noury, N., Rumeau, P., Bourke, A., ÓLaighin, G., & Lundy, J. (2008). A proposal for the classification and evaluation of fall detectors. Innovation and Research in Biomedical engineering (IRBM) 29: 340349.
29.Ordóñez, F.J. & Roggen, D. (2016). Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16: 115.
30.Quero, J., Burns, M., Razzaq, M., Nugent, C., & Espinilla, M. (2018). Detection of falls from non-invasive thermal vision sensors using convolutional neural networks. In Multidisciplinary Digital Publishing Institute Proceedings, volume 2 p. 1236.
31.Riedmiller, M. & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The rprop algorithm. In Proceedings of the IEEE international conference on neural networks, IEEE, pp. 586591.
32.Robbins, H. & Monro, S. (1951). A stochastic approximation method. The annals of mathematical statistics, pp. 400407.
33.Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15: 19291958.
34.Sun, F., Wang, C., Gong, L., Xu, C., Zhang, Y., Lu, Y., Li, X., & Zhou, X. (2017). A power-efficient accelerator for convolutional neural networks. In 2017 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, pp. 631632.
35.Tian, Y., Thompson, J., Buck, D., & Sonola, L. (2013). Exploring the system-wide costs of falls in older people in Torbay. King's Fund.
36.Timotheou, S. (2010). The random neural network: A survey. The computer journal 53: 251267.
37.Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters 119: 311.
38.Xu, T., Zhou, Y., & Zhu, J. (2018). New advances and challenges of fall detection systems: A survey. Applied Sciences 8: 418.
39.Xue-Wen, C. & Xiaotong, L. (2014). Big data deep learning: Challenges and perspectives. IEEE Access 2: 514525.
40.Yin, Y. & Gelenbe, E. (2016a). Deep learning in multi-layer architectures of dense nuclei. arXiv preprint arXiv:1609.07160.
41.Yin, Y. & Gelenbe, E. (2016b). Nonnegative autoencoder with simplified random neural network. arXiv preprint arXiv:1609.08151.
42.Yin, Y. & Gelenbe, E. (2017). Single-cell based random neural network for deep learning. In 2017 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 8693.
43.Zebin, T., Scully, P.J., & Ozanyan, K.B. (2017). Human activity recognition with inertial sensors using a deep learning approach. Proceedings of IEEE Sensors, pp. 13.
44.Zeiler, M.D. (2012). Adadelta: An adaptive learning rate method). arXiv preprint arXiv:1212.5701.



  • Ahsen Tahir (a1), Jawad Ahmad (a2), Gordon Morison (a2), Hadi Larijani (a2), Ryan M. Gibson (a2) and Dawn A. Skelton (a3)...


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed