发帖
雷达卡
摘要翻译：
本文提出并评估了一种用于低功耗嵌入式腕部定位设备的学习体力活动分类器的新算法。整个系统是为实时执行而设计的，并在商用低功耗片上系统nRF51和nRF52中实现。结果是使用一个由140个用户组成的数据库获得的，该数据库包含超过340小时的标记原始加速度数据。对于最重要的类（休息、走路和跑步），最终实现的精确度分别为96%、94%和99%，它概括为复合活动，如XC滑雪或家务劳动。最后，我们对系统的内存占用和功耗进行了基准测试。
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英文标题：
《Learning a Physical Activity Classifier for a Low-power Embedded
Wrist-located Device》
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作者：
Ricard Delgado-Gonzalo, Philippe Renevey, Adrian Tarniceriu, Jakub
Parak, Mattia Bertschi
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最新提交年份：
2017
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
This article presents and evaluates a novel algorithm for learning a physical activity classifier for a low-power embedded wrist-located device. The overall system is designed for real-time execution and it is implemented in the commercial low-power System-on-Chips nRF51 and nRF52. Results were obtained using a database composed of 140 users containing more than 340 hours of labeled raw acceleration data. The final precision achieved for the most important classes, (Rest, Walk, and Run), was of 96%, 94%, and 99% and it generalizes to compound activities such as XC skiing or Housework. We conclude with a benchmarking of the system in terms of memory footprint and power consumption.
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PDF链接：
https://arxiv.org/pdf/1711.02387
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