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Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation
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Abstract
The identification of individual movement characteristics sets the foundation for the assessment of personal rehabilitation progress and can provide diagnostic information on levels and stages of movement disorders. This work presents a preliminary study for differentiating individual motion patterns using a dataset of 3D upper-limb transport trajectories measured in task-space. Identifying individuals by deep time series learning can be a key step to abstracting individual motion properties. In this study, a classification accuracy of about 95% is reached for a subset of nine, and about 78% for the full set of 31 individuals. This provides insights into the separability of patient attributes by exerting a simple standardized task to be transferred to portable systems.
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The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.
Until recently the assessment of many movement disorders has relied on clinical rating scales that despite careful design are inherently subjective and non-linear. This makes accurate and truly observer-independent quantification difficult and limits the use of sensitive parametric statistical methods. At last, devices capable of measuring neurological problems quantitatively are becoming readily available. Examples include the use of oculometers to measure eye movements and accelerometers to measure tremor. Many applications are being developed for use on smartphones. The benefits include not just more accurate disease quantification, but also consistency of data for longitudinal studies, accurate stratification of patients for entry into trials, and the possibility of automated data capture for remote follow-up. In this mini review, we will look at movement disorders with a particular focus on Parkinson's disease, describe some of the limitations of existing clinical evaluation tools, and illustrate the ways in which objective metrics have already been successful.
Activity recognition (AR) and user recognition (UR) using wearable sensors are two key tasks in ubiquitous and mobile computing. Currently, they still face some challenging problems. For one thing, due to the variations in how users perform activities, the performance of a well-trained AR model typically drops on new users. For another, existing UR models are powerless to activity changes, as there are significant differences between the sensor data in different activity scenarios. To address these problems, we propose METIER (deep multi-task learning based activity and user recognition) model, which solves AR and UR tasks jointly and transfers knowledge across them. User-related knowledge from UR task helps AR task to model user characteristics, and activity-related knowledge from AR task guides UR task to handle activity changes. METIER softly shares parameters between AR and UR networks, and optimizes these two networks jointly. The commonalities and differences across tasks are exploited to promote AR and UR tasks simultaneously. Furthermore, mutual attention mechanism is introduced to enable AR and UR tasks to exploit their knowledge to highlight important features for each other. Experiments are conducted on three public datasets, and the results show that our model can achieve competitive performance on both tasks.
Ruhr hand motion catalog of human center-out transport trajectories in 3d taskspace captured by a redundant measurement system
- T Sziburis
- S Blex
- T Glasmachers
- I Iossifidis
T. Sziburis, S. Blex, T. Glasmachers, and I. Iossifidis, "Ruhr hand
motion catalog of human center-out transport trajectories in 3d taskspace captured by a redundant measurement system," 2023.