Derivative Based Gait Event Detection Algorithm Using Unfiltered Accelerometer Signals.

Wearable sensors have been investigated for the purpose of gait analysis, namely gait event detection. Many types of algorithms have been developed specifically using inertial sensor data for detecting gait events. Though much attention has turned toward machine learning algorithms, most of these approaches suffer from large computational requirements and are not yet suitable for real-time applications such as in prostheses or for feedback control.

A Wearable Vibrotactile Biofeedback System Targeting Gait Symmetry of Lower-limb Prosthetic Users.

Lower limb prosthetic users exhibit gait deviations, which include asymmetrical stance time (ST), leading to secondary musculoskeletal problems. Biofeedback (BFB) systems have the potential to provide gait training to correct gait deviations. In this work, we describe a wearable BFB system that delivers vibrotactile feedback via two tactors (located at the anterior and posterior side of the residual limb of prosthetic users) to correct asymmetrical ST (%) using two strategies - single threshold feedback (SF) and bandwidth threshold feedback (BF).

Biofeedback Systems for Gait Rehabilitation of Individuals with Lower-Limb Amputation: A Systematic Review.

Individuals with lower-limb amputation often have gait deficits and diminished mobility function. Biofeedback systems have the potential to improve gait rehabilitation outcomes. Research on biofeedback has steadily increased in recent decades, representing the growing interest toward this topic.