A Multi-Sensor Cane Can Detect Changes in Gait Caused by Simulated Gait Abnormalities and Walking Terrains

Abstract

Due to the increasing rates of chronic diseases and an aging population, the use of assistive devices for ambulation is expected to grow rapidly over the next several years. Instrumenting these devices has been proposed as a non-invasive way to proactively monitor changes in gait due to the presence of pain or a condition in outdoor and indoor environments. In this paper, we evaluated the effectiveness of a multi-sensor cane in detecting changes in gait due to the presence of simulated gait abnormalities, walking terrains, impaired vision, and incorrect cane lengths. The effectiveness of the instrumented cane was compared with the results obtained directly from a shank-mounted inertial measurement unit. Results from 30 healthy participants obtained while simulating gait abnormalities and walking over different terrains demonstrated the ability of the cane to reliably and effectively discriminate among these walking conditions. Moreover, the results obtained while walking with impaired vision and incorrect cane lengths indicate the ability of cane to detect changes in gait during these scenarios as well.

Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

“…Different walking conditions, including impaired vision and walking abnormalities due to incorrect cane lengths of the volunteers, were tested by simulating walking abnormalities. The inertial measurement values were used to classify the walking cycle events [ 84 ].…”

Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation

“…In contrast, as inertial sensors are small and lightweight, they can be easily attached to any point of the cane. Previous inertial sensor-based cane contact phase estimation algorithms can be categorized into three groups: threshold-based methods [ 17 ], event-based methods [ 20 , 21 , 22 ], and machine learning methods. Our proposed method belongs in the third category.…”

“…Similarly, Gill et al [ 21 , 22 ] detected the same gait events by combining the angular velocity with the output signal of a strain gauge attached to the cane. Their method detects an MS event as the positive peak in the angular velocity as in [ 20 ], but detects the IC and EC events by analyzing the zero-crossing points of the strain gauge output rather than the angular velocity.…”

Evaluación de la capacidad de una contera sensorizada para detectar cambios en el patrón de la marcha

“…Estos dispositivos, al ser comúnmente utilizados por personas con problemas de movilidad, no provocan rechazo y han demostrado ser una alternativa eficaz para la captura de datos [4,5,16]. En base a estos datos, algunos estudios han desarrollado algoritmos para clasificar las actividades que se llevan a cabo durante el día [18], otros han trabajado en la detección de caídas [12], e incluso hay estudios que tratan de identificar cambios en el patrón de marcha empleando muletas o andadores sensorizados [2,9].…”