A Study on Linear Acceleration of the Wrist During FreeLiving Accelerometers have gained popularity in biomedical and mhealth applications such as actigraphy or automated dietary monitoring due to their ease of use and their ability to characterize motion. These sensors report raw acceleration from which gravity and linear acceleration must be separated, with commercial packages reporting raw acceleration, linear acceleration or both. New researchers to the field may often be confused when to use raw acceleration or linear acceleration, especially given the susceptibility of linear acceleration to noise, and the lack of published distributions of these signals. This paper provides a short tutorial on obtaining linear acceleration estimates. Using these methods we analyze a large dataset containing 4,680 hours of wrist tracking data, the largest such dataset known to us. We learn the range of wrist motion accelerations, and quantify the expected noise in the linear acceleration signal. We explain the sources of this noise, and a filtering technique to mitigate it. For the first time, we report the range of wrist acceleration values observed during freeliving, and quantify the expected range of noise in this wrist acceleration. We show that while previous work has reported average accelerations at the feet and body ranging from 0xe2x80x9315g during spotslike activities like walking, running or jumping, wrist acceleration in freeliving subjects during daily activities is often much lower, and ranges from 0xe2x80x930.2g. We show that noise in linear acceleration can range from 0xe2x80x930.06g, an overlap of 70%. This suggests that in applications where the wrist acceleration is in this range of noise, linear acceleration may not provide useful features, and researchers should only rely on raw acceleration instead.

Are Accelerometers for Activity Recognition a Deadend? Accelerometerbased (and by extension other inertial sensors) research for Human Activity Recognition (HAR) is a deadend. This sensor does not offer enough information for us to progress in the core domain of HARto 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 informationa 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.  :131,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.