A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson’s disease
Open accessAbstract
Parkinson's disease (PD) is a movement disorder that affects the patient's nervous system and health-care applications mostly uses wearable sensors to collect these data. Since these sensors generate time stamped data, analyzing gait disturbances in PD becomes challenging task. The objective of this paper is to develop an effective clinical decision-making system (CDMS) that aids the physician in diagnosing the severity of gait disturbances in PD affected patients. This paper presents a Q-backpropagated time delay neural network (Q-BTDNN) classifier that builds a temporal classification model, which performs the task of classification and prediction in CDMS. The proposed Q-learning induced backpropagation (Q-BP) training algorithm trains the Q-BTDNN by generating a reinforced error signal. The network's weights are adjusted through backpropagating the generated error signal. For experimentation, the proposed work uses a PD gait database, which contains gait measures collected through wearable sensors from three different PD research studies. The experimental result proves the efficiency of Q-BP in terms of its improved classification accuracy of 91.49%, 92.19% and 90.91% with three datasets accordingly compared to other neural network training algorithms.
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Jane et al. used neural networks to detect PD gait disturbances from wearable accelerometers.
“Jane et al. 12 used neural networks to detect PD gait disturbances from wearable accelerometers.”
From Machine learning for large‐scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures (2016) · cites this paper as [12]
The Experimental settings section describes a neural-network classifier using data from 16 sensors to assess Parkinson’s gait disturbances. But the Dataset description identifies those sensors as force-sensitive devices in shoe insoles measuring vertical ground reaction force, not accelerometers. No page numbers are available in our copy. (LLM, conf=0.90)
Experimental — accuracy not yet measuredJane et al. (2016) reported a classification accuracy of 90.91-92.19% for Parkinson's disease using a Q-back propagated time-delay neural network on gait data from a force transducer in shoe, depending on the input dataset.
“classification accuracy of 90.91-92.19% for PD, depending on the input dataset”
From Machine learning for large‐scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures (2016) · cites this paper as [12]
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- Authors
- Nancy Jane, Y., Khanna Nehemiah, H., & Arputharaj, K.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 60
- Pages
- 169-176
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2272122054
- Cited by
- 98
- References
- 30
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 5 · 2023: 11 · 2022: 14 · 2021: 15 · 2020: 15 · 2019: 9 · 2018: 9 · 2017: 7 · 2016: 3
- FWCI
- 24.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- Parkinson's disease, gait disturbance, time-delay neural networks, Q-learning, clinical decision support systems, wearable sensors, temporal classification
- MeSH terms
- Machine Learning, Algorithms, Female, Humans, Male, Medical Informatics, Parkinson Disease, Neural Networks, Computer, Gait Disorders, Neurologic
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