ArticleSensors (Basel, Switzerland)2022
Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Monitoring BMI-dependent gait adaptations through smart insole technology: implications for musculoskeletal health.BMC sports science, medicine & rehabilitation · 2026Article
- Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.Sensors (Basel, Switzerland) · 2024Review
- Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study.Journal of medical Internet research · 2024Article
- AI-Driven Sensing Technology: Review.Sensors (Basel, Switzerland) · 2024Review
- Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review.Sensors (Basel, Switzerland) · 2023Review
- Insole Systems for Disease Diagnosis and Rehabilitation: A Review.Biosensors · 2023Review
- Open-Source Strain Gauge System for Monitoring Pressure Distribution of Runner's Feet.Sensors (Basel, Switzerland) · 2023Article
- Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a plantar pressure sensor system (P2S2) integrated in the insoles of shoes to detect thirteen commonly used human movements including walking, stooping left and right, pulling a cart backward, squatting, descending, ascending stairs, running, and falling (front, back, right, left). Six force sensitive resistors (FSR) sensors were positioned on critical pressure points on the insoles to capture the electrical signature of pressure change in the various movements. A total of 34 adult participants were tested with the P2S2. The pressure data were collected and processed using a Principal Component Analysis (PCA) for input to the multiple machine learning (ML) algorithms, including k-NN, neural network and Support-Vector Machine (SVM) algorithms. The ML models were trained using four-fold cross-validation. Each fold kept subject data independent from other folds. The model proved effective with an accuracy of 86%, showing a promising result in predicting human movements using the P2S2 integrated in shoes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.