BoostSole: Design and Realization of a Smart Insole for Automatic Human Gait Classification

The operation of wearable robots, such as gait rehabilitation robots, requires real-time classification of the standing or walking state of the wearer. This report explains a technique that measures the ground reaction force (GRF) using an insole device equipped with force sensing resistors, and detects whether the insole wearer is standing or walking based on the measured results. The technique developed in the present study uses the waveform length that represents the sum of the changes in the center of pressure within an arbitrary time window as the determining factor, and applies this factor to a conventional threshold method and an artificial neural network (ANN) model for classification of the standing and walking states. The results showed that applying the newly developed technique could significantly reduce classification errors due to shuffling movements of the patient, typically noticed in the conventional threshold method using GRF, i.e., real-time classification of the standing and walking states is possible in the ANN model. The insole device used in the present study can be applied not only to gait analysis systems used in wearable robot operations, but also as a device for remotely monitoring the activities of daily living of the wearer.

Section: Discussionmentioning

confidence: 99%

Classification of Standing and Walking States Using Ground Reaction Forces

Park

Koo

Kim

2021

“…72 h), while Digitsole [ 30 ] and Arion [ 32 ] offered only 8 and 10 h, respectively, or Mustufa’s et al solution [ 20 ], which provided only 120 min of work. There is no need for device calibration as it works properly from the moment one turns it on—in contrast to many other solutions, such as Arion and Digitsole, as shown in [ 16 , 23 , 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Open-Source Strain Gauge System for Monitoring Pressure Distribution of Runner’s Feet

Kromołowska

Kluza

Kańtoch

et al. 2023

The objective of the research presented in this paper was to provide a novel open-source strain gauge system that shall enable the measurement of the pressure of a runner’s feet on the ground and the presentation of the results of that measurement to the user. The system based on electronic shoe inserts with 16 built-in pressure sensors laminated in a transparent film was created, consisting of two parts: a mobile application and a wearable device. The developed system provides a number of advantages in comparison with existing solutions, including no need for calibration, an accurate and frequent measurement of pressure distribution, placement of electronics on the outside of a shoe, low cost, and an open-source approach to encourage enhancements and open collaboration.

“…Sensors 2023, 23, x FOR PEER REVIEW sensors and 1 bend sensor (black rectangle) [40] (Reprinted with permission from [12], 2023 MDPI, and [40], 2023 IEE Xplore)…”

Section: Sensor Chair (Sitting Ergonomics + Automotive Design)mentioning

confidence: 99%

Human Posture Estimation: A Systematic Review on Force-Based Methods—Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization

Helmstetter,

Matthiesen

2023

Force-based human posture estimation (FPE) provides a valuable alternative when camera-based human motion capturing is impractical. It offers new opportunities for sensor integration in smart products for patient monitoring, ergonomic optimization and sports science. Due to the interdisciplinary research on the topic, an overview of existing methods and the required expertise for their utilization is lacking. This paper presents a systematic review by the PRISMA 2020 review process. In total, 82 studies are selected (59 machine learning (ML)-based and 23 digital human model (DHM)-based posture estimation methods). The ML-based methods use input data from hardware sensors—mostly pressure mapping sensors—and trained ML models for estimating human posture. The ML-based human posture estimation algorithms mostly reach an accuracy above 90%. DHMs, which represent the structure and kinematics of the human body, adjust posture to minimize physical stress. The required expert knowledge for the utilization of these methods and their resulting benefits are analyzed and discussed. DHM-based methods have shown their general applicability without the need for application-specific training but require expertise in human physiology. ML-based methods can be used with less domain-specific expertise, but an application-specific training of these models is necessary.