Are Accelerometers for Activity Recognition a Dead-end?
Are Accelerometers for Activity Recognition a Dead-end?
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DOI：
10.1145/3376897.3377867
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发表时间：
2020-01
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影响因子：
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通讯作者：
C. Tong;Shyam A. Tailor;N. Lane
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文献类型：
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作者：
C. Tong;Shyam A. Tailor;N. Lane
基于加速度计（以及其他惯性传感器）的人类活动识别（HAR）研究是一个死胡同。这个传感器并没有提供足够的信息让我们在HAR的核心领域取得进展——从传感器数据中识别日常活动。尽管在改进特征工程和机器学习模型方面进行了持续和长期的努力，但我们能够可靠地识别的活动只是略微扩大了，许多早期模型的相同缺陷今天仍然存在。我们不应该依赖于加速度数据，而应该考虑具有更丰富信息的模式——一个合乎逻辑的选择是图像。随着图像传感硬件和建模技术的快速发展，我们相信图像传感器的广泛采用将为在广泛的人类活动中进行准确和稳健的推断提供许多机会。在本文中，我们提出了成像仪代替加速度计作为人类活动识别的默认传感器的情况。我们回顾过去的工作，发现HAR的进展已经停滞，这是由于我们对加速度计的依赖造成的。通过说明图像的信息丰富性和计算机视觉的显著进展，我们进一步论证了图像用于活动识别的适用性。通过可行性分析，我们发现在设备上部署成像仪和cnn对现代移动硬件没有太大的负担。总的来说，我们的工作强调了摆脱加速度计的需要，并呼吁进一步探索使用成像仪进行活动识别。
Accelerometer-based (and by extension other inertial sensors) research for Human Activity Recognition (HAR) is a dead-end. This sensor does not offer enough information for us to progress in the core domain of HAR - to recognize everyday activities from sensor data. Despite continued and prolonged efforts in improving feature engineering and machine learning models, the activities that we can recognize reliably have only expanded slightly and many of the same flaws of early models are still present today. Instead of relying on acceleration data, we should instead consider modalities with much richer information - a logical choice are images. With the rapid advance in image sensing hardware and modelling techniques, we believe that a widespread adoption of image sensors will open many opportunities for accurate and robust inference across a wide spectrum of human activities. In this paper, we make the case for imagers in place of accelerometers as the default sensor for human activity recognition. Our review of past works has led to the observation that progress in HAR had stalled, caused by our reliance on accelerometers. We further argue for the suitability of images for activity recognition by illustrating their richness of information and the marked progress in computer vision. Through a feasibility analysis, we find that deploying imagers and CNNs on device poses no substantial burden on modern mobile hardware. Overall, our work highlights the need to move away from accelerometers and calls for further exploration of using imagers for activity recognition.