Biomedical signal quality analysis for wearable technologies

技术的进步带来了新的生物医学监测设备，包括可穿戴设备，可以进行连续、长期和动态监测，并具有实时反馈的潜力。可穿戴设备有着广泛的应用，包括健康、健身、人机交互和康复。可穿戴设备市场正在快速增长（复合年增长率为37%），到2021年，设备收入将达到近1000亿美元。******与由专家在良好控制环境中进行的传统生物医学监测不同，可穿戴设备通常由非专业最终用户在各种不受控制的环境中使用。因此，可穿戴设备极易受到各种污染物（即噪音和伪影）的影响。受污染的生物医学数据可能导致对数据的误解，包括误诊和误报。糟糕的数据质量是可穿戴设备被主流采用的一个重大障碍。******该研究项目支持个人生物医学设备的开发，包括可穿戴设备。特别是，该研究旨在开发生物医学信号质量分析方法，以提高可穿戴设备的准确性和鲁棒性，并解决几个重要的研究空白。该研究在不断发展的生物医学信号质量分析领域推进了最先进的技术，开发了新的方法来检测、识别、量化和模拟可穿戴设备记录的数据中的污染物。该研究采用了先进的机器学习方法，包括深度学习，可以提供卓越的性能。它还研究了当前尖端环境下的生物医学信号质量分析主题，例如压缩感知，这对于可穿戴设备来说非常重要，因为它可以减少存储、带宽和功耗要求。******拟议的研究将对可穿戴设备产生重要影响，解决数据质量差的基本问题。例如，研究成果可用于使可穿戴系统忽略低质量的数据段，避免误解，同时保留有效和有用的生物医学数据。提高可穿戴设备信息利用方法的准确性和稳健性对于这些设备的实用性、用户体验和用户接受度至关重要

Advances in technology have led to new biomedical monitoring devices, including wearables, which allow for continuous, long-term, and ambulatory monitoring, along with the potential for real-time feedback. Wearables have broad applications, including health and wellness, fitness, human-computer interaction, and rehabilitation. The wearables market is rapidly growing (37% compounded annual growth) with device revenues reaching almost $100 billion by 2021.******Unlike conventional biomedical monitoring, performed by experts in well-controlled environments, wearables are often used by non-expert end-users in a diverse set of uncontrolled environments. As a result, wearables are highly susceptible to a variety of contaminants (i.e., noise and artifacts). Contaminated biomedical data can result in misinterpretation of the data, including misdiagnoses and false alarms. Poor data quality is a significant barrier for mainstream adoption of wearables.******This research program supports the development of personal biomedical devices, including wearables. In particular, the proposed research aims to develop biomedical signal quality analysis methods that will improve the accuracy and robustness of wearable devices and addresses several important research gaps. The research advances the state-of-the art, within the growing area of biomedical signal quality analysis, developing novel approaches to detect, identify, quantify, and model contaminants in data recorded by wearables. The research employs advanced machine learning approaches, including deep learning, which can offer superior performance. It also investigates the topic of biomedical signal quality analysis within a current, cutting-edge context, such as compressive sensing, which can be important for wearables at it reduces storage, bandwidth, and power requirements.******The proposed research will have an important impact for wearables, addressing fundamental issues of poor data quality. For example, research outcomes can be used to enable wearable systems to disregard data segments of poor quality, avoiding misinterpretations, while retaining valid and useful biomedical data. The increased accuracy and robustness of information-leveraging methods for wearable devices is vital for the utility, user-experience, and user-acceptance of these devices.**