Privacy-Preserved Human Motion Analysis for Healthcare Applications

适用于医疗保健应用的保护隐私的人体运动分析

• 批准号: EP/W01212X/1
• 负责人: Fani Deligianni
• 金额: $ 40.58万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW01212X%2F1
• 关键词: Privacy; Preserved; Human; Motion; Analysis

Human motion analysis is a powerful tool in healthcare applications as it has shown to be effective in providing disease progression markers in neurodegenerative conditions such as Alzheimer's, Parkinson, Amyotrophic Lateral Sclerosis, Huntington's disease and dementia. On the other hand, deep learning in human motion analysis has shown impressive results in human pose tracking in real-time. This technology can empower patients to have an active role in managing their condition(s), which is a significant objective in a growing e-Health (digital Health) era. Opportunities in digital health initiatives have increased through the response to the pandemic and it has become evident of the need for an intelligent system to detect abnormal changes in patient gait patterns and subsequently alert carers. This technology can also prevent further deterioration (multimorbidity) due to the associated risk of falls and mood disorders. However, translating recent advances in computer vision in home care is challenging for three major reasons: data privacy, lack of large healthcare labelled data and reduced data quality. This project proposes that data privacy and ethics should be encoded in the algorithms early in the pipeline so that systems are resilient to attacks and do not compromise real-time interaction. We argue that this approach could also improve the performance of the machine learning models with small datasets by focusing on the most relevant features in a data-driven way. Furthermore, we propose that coupling this technology with synthetic data generation can significantly boost the development of ambient sensing technologies for human motion tracking in healthcare applications and develop technology viable for the UK market.

人体运动分析是医疗保健应用中的强大工具，因为它已被证明在提供神经退行性疾病（例如阿尔茨海默氏症、帕金森氏症、肌萎缩性侧索硬化症、亨廷顿氏病和痴呆症）中的疾病进展标记物方面是有效的。另一方面，人体运动分析中的深度学习在实时人体姿势跟踪方面显示出令人印象深刻的结果。这项技术可以使患者在管理其病情方面发挥积极作用，这是不断发展的电子健康（数字健康）时代的一个重要目标。通过应对大流行，数字健康计划的机会增加了，显然需要一个智能系统来检测患者步态模式的异常变化，并随后提醒护理人员。这项技术还可以防止由于福尔斯和情绪障碍的相关风险而导致的进一步恶化（多Mortality）。然而，将计算机视觉的最新进展转化为家庭护理具有挑战性，主要原因有三个：数据隐私，缺乏大型医疗保健标签数据和数据质量降低。该项目提出，数据隐私和道德应该在管道的早期编码在算法中，以便系统能够抵御攻击，并且不会损害实时交互。我们认为，这种方法还可以通过以数据驱动的方式关注最相关的特征，来提高小数据集的机器学习模型的性能。此外，我们建议将该技术与合成数据生成相结合，可以显着促进环境传感技术在医疗保健应用中的人体运动跟踪的发展，并为英国市场开发可行的技术。

项目成果

Adversarial Attention for Human Motion Synthesis

• DOI: 10.1109/ssci52147.2023.10371870
• 发表时间: 2022-04
• 期刊: 2023 IEEE Symposium Series on Computational Intelligence (SSCI)
• 作者: Matthew Malek-Podjaski;F. Deligianni

Toward Personalized Music-Therapy: A Neurocomputational Modeling Perspective

• DOI: 10.1109/mprv.2023.3285087
• 发表时间: 2023-06-30
• 期刊: IEEE PERVASIVE COMPUTING
• 影响因子: 1.6
• 作者: Lai-Tan，Nicole;Philiastides，Marios G. G.;Deligianni，Fani
• 通讯作者: Deligianni，Fani

Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation

• DOI: 10.48550/arxiv.2306.14293
• 发表时间: 2023-06
• 作者: Qianying Liu;Xiao Gu;Paul Henderson;F. Deligianni

Threat Perception Captured by Emotion, Motor and Empathetic System Responses: A Systematic Review

• DOI: 10.1109/taffc.2023.3323043
• 发表时间: 2024-07-01
• 期刊: IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
• 影响因子: 11.2
• 作者: Jacobs，Elizabeth Michelle;Deligianni，Fani;Pollick，Frank
• 通讯作者: Pollick，Frank