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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.10661 |
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| _version_ | 1866917641722003456 |
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| author | Kandel, Jade Duppen, Chelsea Zhang, Qian Jiang, Howard Angelopoulos, Angelos Neall, Ashley Wagh, Pranav Szafir, Daniel Fuchs, Henry Lewek, Michael Szafir, Danielle Albers |
| author_facet | Kandel, Jade Duppen, Chelsea Zhang, Qian Jiang, Howard Angelopoulos, Angelos Neall, Ashley Wagh, Pranav Szafir, Daniel Fuchs, Henry Lewek, Michael Szafir, Danielle Albers |
| contents | People with Parkinson's Disease (PD) can slow the progression of their symptoms with physical therapy. However, clinicians lack insight into patients' motor function during daily life, preventing them from tailoring treatment protocols to patient needs. This paper introduces PD-Insighter, a system for comprehensive analysis of a person's daily movements for clinical review and decision-making. PD-Insighter provides an overview dashboard for discovering motor patterns and identifying critical deficits during activities of daily living and an immersive replay for closely studying the patient's body movements with environmental context. Developed using an iterative design study methodology in consultation with clinicians, we found that PD-Insighter's ability to aggregate and display data with respect to time, actions, and local environment enabled clinicians to assess a person's overall functioning during daily life outside the clinic. PD-Insighter's design offers future guidance for generalized multiperspective body motion analytics, which may significantly improve clinical decision-making and slow the functional decline of PD and other medical conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10661 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PD-Insighter: A Visual Analytics System to Monitor Daily Actions for Parkinson's Disease Treatment Kandel, Jade Duppen, Chelsea Zhang, Qian Jiang, Howard Angelopoulos, Angelos Neall, Ashley Wagh, Pranav Szafir, Daniel Fuchs, Henry Lewek, Michael Szafir, Danielle Albers Human-Computer Interaction People with Parkinson's Disease (PD) can slow the progression of their symptoms with physical therapy. However, clinicians lack insight into patients' motor function during daily life, preventing them from tailoring treatment protocols to patient needs. This paper introduces PD-Insighter, a system for comprehensive analysis of a person's daily movements for clinical review and decision-making. PD-Insighter provides an overview dashboard for discovering motor patterns and identifying critical deficits during activities of daily living and an immersive replay for closely studying the patient's body movements with environmental context. Developed using an iterative design study methodology in consultation with clinicians, we found that PD-Insighter's ability to aggregate and display data with respect to time, actions, and local environment enabled clinicians to assess a person's overall functioning during daily life outside the clinic. PD-Insighter's design offers future guidance for generalized multiperspective body motion analytics, which may significantly improve clinical decision-making and slow the functional decline of PD and other medical conditions. |
| title | PD-Insighter: A Visual Analytics System to Monitor Daily Actions for Parkinson's Disease Treatment |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.10661 |