ArticleSensors (Basel, Switzerland)2022
Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- Demographic Shortcuts Account for Most of the Apparent Accuracy of Four-Class Gait-Based Differential Diagnosis in Neurodegenerative Disease: An Interpretability-Driven Audit of Machine Learning on a Public Gait Benchmark.Bioengineering (Basel, Switzerland) · 2026Article
- Wearable-Sensor-Based Analysis of Aerial Archimedean Spirals for Early Detection of Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Uncovering the Kinematic Signature of Freezing of Gait in Parkinson's Disease Through Wearable Inertial Sensors.Sensors (Basel, Switzerland) · 2025Article
- Diagnosing Parkinson's disease via behavioral biometrics of keystroke dynamics.Science advances · 2025Article
- Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach.Sensors (Basel, Switzerland) · 2025Article
- Article
- Implementing sensor-based digital health technologies in clinical trials: Key considerations from the eCOA Consortium.Clinical and translational science · 2024Review
- Convolutional neural network based detection of early stage Parkinson's disease using the six minute walk test.Scientific reports · 2024Article
- A novel machine learning based framework for developing composite digital biomarkers of disease progression.Frontiers in digital health · 2024Article
- Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.
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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.