A Neural Network for Stance Phase Detection in Smart Cane Users

Persons with disabilities often rely on assistive devices to carry on their Activities of Daily Living. Deploying sensors on these devices may provide continuous valuable knowledge on their state and condition. Canes are among the most frequently used assistive devices, regularly employed for ambulation by persons with pain on lower limbs and also for balance. Load on canes is reportedly a meaningful condition indicator. Ideally, it corresponds to the time cane users support weight on their lower limb (stance phase). However, in reality, this relationship is not straightforward. We present a Multilayer Perceptron to reliably predict the Stance Phase in cane users using a simple support detection module on commercial canes. The system has been successfully tested on five cane users in care facilities in Spain. It has been optimized to run on a low cost microcontroller.

[1]  Richard Lippmann,et al.  Neural Net and Traditional Classifiers , 1987, NIPS.

[2]  S Sapna,et al.  Backpropagation Learning Algorithm Based on Levenberg Marquardt Algorithm , 2012 .

[3]  Kara K. Patterson,et al.  Gait asymmetry in community-ambulating stroke survivors. , 2008, Archives of physical medicine and rehabilitation.

[4]  Pouria Khosravi,et al.  Investigating the effectiveness of technologies applied to assist seniors: A systematic literature review , 2016, Int. J. Medical Informatics.

[5]  Young Soo Suh,et al.  Walking Distance Estimation Using Walking Canes with Inertial Sensors , 2018, Sensors.

[6]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[7]  Cristina Urdiales,et al.  Weight-Bearing Estimation for Cane Users by Using Onboard Sensors , 2019, Sensors.

[8]  Lukás Burget,et al.  Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Ronald C Petersen,et al.  Normative spatiotemporal gait parameters in older adults. , 2011, Gait & posture.

[10]  Megan Corlis,et al.  ‘I feel less lonely’: what older people say about participating in a social networking website , 2010 .

[11]  Kushang V Patel,et al.  Mobility Device Use in Older Adults and Incidence of Falls and Worry About Falling: Findings from the 2011–2012 National Health and Aging Trends Study , 2015, Journal of the American Geriatrics Society.

[12]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[13]  Nicolae Morariu,et al.  A Neural Network Model for Time-Series Forecasting , 2009 .

[14]  B. Weiss,et al.  Ambulatory devices for chronic gait disorders in the elderly. , 2003, American family physician.

[15]  Pietro Valdastri,et al.  Design and implementation of an instrumented cane for gait recognition , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Sara M. Bradley,et al.  Geriatric assistive devices. , 2011, American family physician.

[17]  Diane J. Cook,et al.  Quantitative assessment of lower limb and cane movement with wearable inertial sensors , 2016, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).

[18]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[19]  Zachary Chase Lipton.  A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[20]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[21]  M. Wong,et al.  Temporal stride and force analysis of cane-assisted gait in people with hemiplegic stroke. , 2001, Archives of physical medicine and rehabilitation.

[22]  Achille Fokoue,et al.  An effective algorithm for hyperparameter optimization of neural networks , 2017, IBM J. Res. Dev..

[23]  Hao Yu,et al.  Selection of Proper Neural Network Sizes and Architectures—A Comparative Study , 2012, IEEE Transactions on Industrial Informatics.

[24]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[25]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[26]  Luca Palmerini,et al.  Assessing Gait in Parkinson’s Disease Using Wearable Motion Sensors: A Systematic Review , 2019, Diseases.

[27]  Jesse Hoey,et al.  Engaging Older Adults with Dementia in Creative Occupations Using Artificially Intelligent Assistive Technology , 2013, Assistive technology : the official journal of RESNA.