Standardization of body weight distribution using a Force-Sensitive Resistor (FSR) matrix and foot analytics
5 affiliations
- 1Centre for AI and Big Data, Namal University, Mianwali, Pakistan
- 2Department of Electrical Engineering, Namal University, Mianwali, Pakistan
- 3Pakistan Supercomputing Center, and PakASIC, Islamabad, Pakistan
- 4College of Engineering Department of Electrical and Electronic Engineering, University of Jeddah, Jeddah, Saudi Arabia
- 5Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
Abstract
Balanced body weight distribution across both feet is essential for maintaining musculoskeletal health and minimizing postural and ambulatory issues. Conventionally foot weight distribution is measured using specialized clinical equipment which are expensive and limits routine screening. Therefore, in this work, we present a standardized foot weight distribution model having a custom 32×32 force-sensing resistor (FSR) matrix based embedded system. A total of 4,000 volunteers participated in the Physical Fitness Assessment (PFA). Of these, 2,200 who passed the musculoskeletal fitness criteria were selected for foot pressure data collection. From these data, we derived a standard weight distribution pattern across foot regions: heel (45–55%), midfoot (10–15%), metatarsals (17–27%), and toes (8–13%). These standardized weight distribution patterns are used to identify deviations associated with musculoskeletal conditions that support early personalized rehabilitation and training strategies. The proposed model is implemented on a real-time embedded system and cloud platform. The embedded system uses a K230 RISC-V processor and performs real-time data acquisition, on-board data processing, and visualization of foot weight distribution. The application running on cloud platform performs statistical analysis and applies a classical support vector machine (SVM) for classifying abnormal pressure patterns. A Convolution Neural Network (CNN) based model is developed that learns fit and un-fit foot patterns from the 32×32 pressure maps. The model results show improved detection of dysfunctional weight distribution. The CNN approach shows that AI model can perform advanced predictive analytics in gait and posture assessment. The analysis is limited to static standing posture and does not include dynamic gait assessment. The normative ranges are derived primarily from subjects up to approximately 80–90 kg, as no musculoskeletally fit participants were identified in the heavier weight groups (90–120 kg); therefore, generalization to heavier individuals requires caution and further data collection.
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BibTeX
@article{hussain2026standardization,
title = {{Standardization of body weight distribution using a Force-Sensitive Resistor (FSR) matrix and foot analytics}},
author = {Tassadaq Hussain and Soltan Alharbi and Ali Tahir and Mutaz Elradi S. Saeed and Usman Masud},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0358764},
url = {https://doi.org/10.1371/journal.pone.0358764},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Standardization of body weight distribution using a Force-Sensitive Resistor (FSR) matrix and foot analytics
AU - Tassadaq Hussain
AU - Soltan Alharbi
AU - Ali Tahir
AU - Mutaz Elradi S. Saeed
AU - Usman Masud
PY - 2026
DA - 2026/10/01
JO - PLOS One
DO - 10.1371/journal.pone.0358764
UR - https://doi.org/10.1371/journal.pone.0358764
AB - Balanced body weight distribution across both feet is essential for maintaining musculoskeletal health and minimizing postural and ambulatory issues. Conventionally foot weight distribution is measured using specialized clinical equipment which are expensive and limits routine screening. Therefore, in this work, we present a standardized foot weight distribution model having a custom 32×32 force-sensing resistor (FSR) matrix based embedded system. A total of 4,000 volunteers participated in the Physical Fitness Assessment (PFA). Of these, 2,200 who passed the musculoskeletal fitness criteria were selected for foot pressure data collection. From these data, we derived a standard weight distribution pattern across foot regions: heel (45–55%), midfoot (10–15%), metatarsals (17–27%), and toes (8–13%). These standardized weight distribution patterns are used to identify deviations associated with musculoskeletal conditions that support early personalized rehabilitation and training strategies. The proposed model is implemented on a real-time embedded system and cloud platform. The embedded system uses a K230 RISC-V processor and performs real-time data acquisition, on-board data processing, and visualization of foot weight distribution. The application running on cloud platform performs statistical analysis and applies a classical support vector machine (SVM) for classifying abnormal pressure patterns. A Convolution Neural Network (CNN) based model is developed that learns fit and un-fit foot patterns from the 32×32 pressure maps. The model results show improved detection of dysfunctional weight distribution. The CNN approach shows that AI model can perform advanced predictive analytics in gait and posture assessment. The analysis is limited to static standing posture and does not include dynamic gait assessment. The normative ranges are derived primarily from subjects up to approximately 80–90 kg, as no musculoskeletally fit participants were identified in the heavier weight groups (90–120 kg); therefore, generalization to heavier individuals requires caution and further data collection.
SN - 1932-6203
ER -