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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health
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A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health

机译：一种独立于设备的有效书法信号编码系统用于监测人类日常活动和健康

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摘要

Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.
机译：用于个性化的健康和健身监控的Actigraphs是一个新兴的利基市场，并且恰好适合医疗物联网（IoMT）范例。常规地，使用标准的低通滤波和量化技术来获取书法并将其数字化。各种活动记录仪的高采样频率和量化分辨率会导致内存泄漏和不必要的电池使用。我们对不同类型的书法信号进行的系统研究表明，较低的量化水平足以获取和存储重要的运动信息，同时确保SNR的提高，更大的空间节省和更快的时间。这项研究的目的是提出一种低级信号编码方法，该方法可以改善活动记录仪中的数据采集和存储，并增强模式分类的信号清晰度。为了进一步验证这项研究，我们使用了一种机器学习方法，该方法表明信号编码还可以提高模式识别的准确性。我们的实验表明，在信号源处进行信号编码会导致SNR（信噪比）至少提高50-90％，同时比特率降低50-80％，并且总体上节省了空间。范围为68–92％，具体取决于本研究中使用的活动记录仪类型和应用。通过降低量化因子进行的持续改进还表明，书法数据的3位编码保留了最突出的运动信息，并且还使模式识别精度提高了至少10％。

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