ArticleJournal of neuroengineering and rehabilitation2022
Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks.
Article in Journal of neuroengineering and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 41 citations in OpenAlex.
- Deep learning for freezing of gait assessment using inertial measurement units: a multicentre validation study.NPJ Parkinson's disease · 2026Article
- Freezing of Gait in Parkinson's Disease: A Scoping Review on the Path Towards Real-Time Therapies.Sensors (Basel, Switzerland) · 2026Article
- Uncovering the Kinematic Signature of Freezing of Gait in Parkinson's Disease Through Wearable Inertial Sensors.Sensors (Basel, Switzerland) · 2025Article
- Towards Explainable Graph Embeddings for Gait Assessment Using Per-Cluster Dimensional Weighting.Sensors (Basel, Switzerland) · 2025Article
- Scoping Review of Machine Learning Techniques in Marker-Based Clinical Gait Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait.Journal of medical Internet research · 2025Article
- Technology for measuring freezing of gait: Current state of the art and recommendations.Journal of Parkinson's disease · 2025Review
- Article
- Gait-Guard: Turn-aware Freezing of Gait Detection for Non-intrusive Intervention Systems....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2024Article
- Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops.Journal of neuroengineering and rehabilitation · 2024Article
- Is the freezing index a valid outcome to assess freezing of gait during turning in Parkinson's disease?Frontiers in neurology · 2024Article
- A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms.Neurons, behavior, data analysis, and theory · 2024Article
- Overview on wearable sensors for the management of Parkinson's disease.NPJ Parkinson's disease · 2023Review
- Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics.Sensors (Basel, Switzerland) · 2023Article
- FoG-Finder: Real-time Freezing of Gait Detection and Treatment....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2023Article
- Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems.Scientific reports · 2023Article
- Modelling and identification of characteristic kinematic features preceding freezing of gait with convolutional neural networks and layer-wise relevance propagation.BMC medical informatics and decision making · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFreezing of gait (FOG) is a common and debilitating gait impairment in Parkinson's disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess FOG. To meet this clinical need, this paper proposes an automated motion-capture-based FOG assessment method driven by a novel deep neural network.
methodsAutomated FOG assessment can be formulated as an action segmentation problem, where temporal models are tasked to recognize and temporally localize the FOG segments in untrimmed motion capture trials. This paper takes a closer look at the performance of state-of-the-art action segmentation models when tasked to automatically assess FOG. Furthermore, a novel deep neural network architecture is proposed that aims to better capture the spatial and temporal dependencies than the state-of-the-art baselines. The proposed network, termed multi-stage spatial-temporal graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The ST-GCN captures the hierarchical spatial-temporal motion among the joints inherent to motion capture, while the multi-stage component reduces over-segmentation errors by refining the predictions over multiple stages. The proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects.
resultsThe experiments indicate that the proposed model outperforms four state-of-the-art baselines. Moreover, FOG outcomes derived from MS-GCN predictions had an excellent (r = 0.93 [0.87, 0.97]) and moderately strong (r = 0.75 [0.55, 0.87]) linear relationship with FOG outcomes derived from manual annotations.
conclusionsThe proposed MS-GCN may provide an automated and objective alternative to labor-intensive clinician-based FOG assessment. Future work is now possible that aims to assess the generalization of MS-GCN to a larger and more varied verification cohort.
Indexed as
Identifiers
What OpenQuestion holds
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.