Gait Analysis and Severity Estimation of Parkinson’s Disease Patients Using Insole-Type Pressure Sensors

Parkinson’s disease (PD) is an intractable neurological disease that affects approximately 100-150 per 100, 000 people in Japan, with more than 95% of these patients aged 60 years or older. The Hoehn and Yahr staging scale (H-Y scale) and Unified Parkinson’s Disease Rating Scale (UPDRS) score are typical indicators of PD severity, but subjective aspects cannot be completely excluded. This is due to the limited means to quantitatively measure and evaluate gait as a motor symptom of PD. However, in recent years, human movement has been measured by wearable devices and other devices in daily life. In this paper, we took this wearable-device measurement one step further and aimed to apply insole-type pressure sensors to medical care. We collected data from two patients with PD at H-Y stage 2 and nine patients with PD at H-Y stage 3, then generated a time-series pattern representing their gait patterns. By analyzing these gait patterns, we found that the sum of the three toe sensors and the sum of the two heel sensors provided stable data. The overlap time of the two summed sensor values and the deviations of each summed sensor value were defined as features. Using the features, we proposed PD severity estimation based on the k-means method. Experimental results showed that the estimation of Yahr 3 and healthy individuals was achieved with high accuracy, but Yahr 2 was often classified as a healthy individual.

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Publication Details

Journal
IEICE Transactions on Information and Systems
Published
2025-04-03
DOI
https://doi.org/10.1587/transinf.2024edp7285
Primary Topic
Parkinson's Disease Mechanisms and Treatments
Type
article
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article

Gait Analysis and Severity Estimation of Parkinson’s Disease Patients Using Insole-Type Pressure Sensors

Mami Takemoto, Masanobu Abe, Sunao Hara, Toru Yamashita et al.
IEICE Transactions on Information and Systems
Parkinson's Disease Mechanisms and Treatments
article

Gait Analysis and Severity Estimation of Parkinson’s Disease Patients Using Insole-Type Pressure Sensors

Mami Takemoto, Masanobu Abe, Sunao Hara, Toru Yamashita, K. Hayashi
article en

Abstract

Parkinson’s disease (PD) is an intractable neurological disease that affects approximately 100-150 per 100, 000 people in Japan, with more than 95% of these patients aged 60 years or older. The Hoehn and Yahr staging scale (H-Y scale) and Unified Parkinson’s Disease Rating Scale (UPDRS) score are typical indicators of PD severity, but subjective aspects cannot be completely excluded. This is due to the limited means to quantitatively measure and evaluate gait as a motor symptom of PD. However, in recent years, human movement has been measured by wearable devices and other devices in daily life. In this paper, we took this wearable-device measurement one step further and aimed to apply insole-type pressure sensors to medical care. We collected data from two patients with PD at H-Y stage 2 and nine patients with PD at H-Y stage 3, then generated a time-series pattern representing their gait patterns. By analyzing these gait patterns, we found that the sum of the three toe sensors and the sum of the two heel sensors provided stable data. The overlap time of the two summed sensor values and the deviations of each summed sensor value were defined as features. Using the features, we proposed PD severity estimation based on the k-means method. Experimental results showed that the estimation of Yahr 3 and healthy individuals was achieved with high accuracy, but Yahr 2 was often classified as a healthy individual.

IEICE Transactions on Information and SystemsVol. E108.D(10)
Okayama University (JP), Fujitsu (Japan) (JP), Okayama University Hospital (JP), Fujitsu (China) (CN)
Openalex Percentile: Top 89%
Parkinson's Disease Mechanisms and Treatments
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Gait Analysis and Severity Estimation of Parkinson’s Disease Patients Using Insole-Type Pressure Sensors — Mami Takemoto, Masanobu Abe, et al. · IEICE Transactions on Information and Systems (2025) | TGRS Research Map | TGRS