RT Conference Proceedings
T1 A Neural Network for Stance Phase detection in smart cane users
A1 Caro-Romero, Juan
A1 Ballesteros-Gómez, Joaquín
A1 García-Lagos, Francisco
A1 Urdiales-García, Amalia Cristina
A1 Sandoval-Hernández, Francisco
K1 Sensores
K1 Movimiento
AB 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.
YR 2019
FD 2019-06-10
LK https://hdl.handle.net/10630/17785
UL https://hdl.handle.net/10630/17785
LA eng
NO Slides from conference
NO This work has been supported by: Proyectos Puente and programa operativo de empleo juvenil (UMAJI58) and Plan Propio de Investigación at University of Malaga and the Swedish Knowledge Foundation (KKS) through the research profile Embedded Sensor Systems for Health (ESS−H) at Malardalen University, Sweden. Authors would like to ac- knowledge PONIENTE and LOS NARANJOS senior centers for their support during the tests. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026