Continuous gait monitoring is important in Parkinson’s Disease (PD) as gait deteriorates with disease progression and responses to treatment vary over time. Wearable sensor systems offer a practical alternative to laboratory-based gait assessment for real-world monitoring. This study aimed to (1) validate spatiotemporal gait parameters derived from the NUSHU smart shoe system against the Vicon motion capture system in healthy older adults and individuals with PD, and (2) assess whether measurement agreement was maintained during active vibrotactile feedback delivery. Thirty-two participants (17 with mild to moderate PD and 15 age- and gender-matched healthy older adults; mean age 67 ± 7.4 years) completed overground walking trials under no vibration, stance phase vibration, and swing phase vibration conditions. A total of 4790 strides were analysed using linear mixed effects models. Overall agreement was good for stride length (ICC = 0.894, CCC = 0.926), stride time (ICC = 0.853, CCC = 0.954), and velocity (ICC = 0.885, CCC = 0.940), with moderate agreement for double support time (ICC = 0.752, CCC = 0.477). Agreement remained consistent across vibration conditions in both groups.

Walking impairments are a major cause of reduced mobility, falls, and loss of independence in individuals with neurological disorders and age-related mobility decline. As the demand for effective rehabilitation continues to grow, there is increasing interest in technologies that can support gait training beyond the clinical setting.

Wearable vibrotactile feedback is one such approach. By delivering precisely timed tactile cues during walking, these systems augment the sensory information available to the nervous system, supporting gait control and motor learning rather than replacing natural movement. A growing body of research demonstrates that vibration can influence proprioception, enhance sensorimotor processing, and improve clinically relevant aspects of gait.

This paper reviews the scientific principles underlying wearable vibrotactile feedback, summarizes the current clinical evidence, and discusses how these findings support the development of wearable rehabilitation technologies for improving gait and functional mobility.

Walking is a fundamental part of everyday life, playing an important role in mobility and independence. Although it often feels automatic, each step requires constant coordination between the nervous system, sensory systems and muscles.

Movement relies on an ongoing sensorimotor loop: the nervous system coordinates movement while receiving continuous sensory feedback from the body and environment to help guide and adapt each step. Changes in gait may result when this communication is affected by neurological injury or disease.

Objective gait measurement can describe characteristics such as walking speed, step length, timing, symmetry and variability. Considering multiple gait parameters and how they change over time can provide a more detailed picture of how a person moves.

Humans use sensory information continuously to perceive their surroundings, understand the position and movement of their body, and adapt to a changing environment. While sight, hearing, smell, taste, and touch are familiar to most of us, less familiar senses also play an important role in how we move.

Walking is a remarkable example of sensory information working together. Vision provides information about the environment, proprioception about the position and movement of the body, and the vestibular system about movement and orientation. The nervous system continuously integrates this information to guide and adapt movement. Understanding this interaction also introduces the concept of sensory augmentation, in which additional sensory information, such as vibrotactile feedback, can become part of this ongoing exchange.

Falls are complex events influenced by many interacting factors. Since walking requires coordination of balance, sensory information, muscle function, attention, and movement, changes in gait can provide measurable data about how a person walks. Research has identified several gait characteristics associated with an increased risk of falling, including slower walking speed, shorter steps and strides, longer double-support time, and greater gait variability.

However, no single gait parameter can determine whether someone will fall. Instead, gait measurements provide different pieces of information about walking performance. Considering multiple gait characteristics, documenting how they change over time, and measuring gait during standardized assessments and everyday walking provides a broader picture of how a person moves.

Automatic methods for detecting FOG using the freeze index (FI) have been widely proposed to systematically monitor FOG in real life and guide therapy optimizations. However, methods to estimate the FI have relied on a broad range of measurement technologies and computational methodologies, often lacking mathematical rigor. This lack of standardization has severely hindered the acceptance of FI by regulatory agencies as a reproducible, robust, effective and safe measure on which to base further developments. In this study, we formalize the definition of the FI and propose a rigorous, explicit estimation algorithm, which may serve as a standard for future applications. This standardization provides a consistent and reliable benchmark.

Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems. To date, their reliability and limitations in manual labeling of gait events have not been studied.

Objectives: Evaluate manual labeling uncertainty and introduce a hybrid stride detection and gait-event estimation model for autonomous, long-term, and remote monitoring.

Methods: Estimate inter-labeler inconsistencies by computing the limits-of-agreement. Develop a hybrid model based on dynamic time warping and convolutional neural network to identify valid strides and eliminate non-stride data in inertial (walking) data collected by a wearable device. Finally, detect gait events within a valid stride region.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases.
Continuous gait monitoring and analysis are essential for early deficit detection and personalized reha‐
bilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have
been well explored with promising results in the literature. In this work, we developed an
inertial sensor based in‐shoe gait analysis system for real‐time gait monitoring and investigated the
optimal sampling frequency to capture all the information on walking patterns. An exploratory valida‐
tion study was performed using an optical motion capture system on four healthy adult subjects.