For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.

The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.

In recent years, the use of a smartphone accelerometer in physical activity recognition has been well studied. However, the role of a gyroscope and a magnetometer is yet to be explored, both when used alone as well as in combination with an accelerometer. For this purpose, we investigate the role of these three smartphone sensors in activity recognition. We evaluate their roles on four body positions using seven classifiers while recognizing six physical activities. We show that in general an accelerometer and a gyroscope complement each other, thereby making the recognition process more reliable. Moreover, in most cases, a gyroscope does not only improve the recognition accuracy in combination with an accelerometer, but it also achieves a reasonable performance when used alone. The results for a magnetometer are not encouraging because it causes over-fitting in training classifiers due to its dependence on directions. Based on our evaluations, we show that it is difficult to make an exact general statement about which sensor performs better than the others in all situations because their recognition performance depends on the smartphone's position, the selected classifier, and the activity being recognized. However, statements about their roles in specific situations can be made. We report our observations and results in detail in this paper, while our data-set and datacollection app is publicly available, thereby making our experiments reproducible.

Recently, there has been a growing interest in the research community about using wrist-worn devices, such as smartwatches for human activity recognition, since these devices are equipped with various sensors such as an accelerometer and a gyroscope. Similarly, smartphones are already being used for activity recognition. In this paper, we study the fusion of a wristworn device (smartwatch) and a smartphone for human activity recognition. We evaluate these two devices for their strengths and weaknesses in recognizing various daily physical activities. We use three classifiers to recognize 13 different activities, such as smoking, cannot be recognized with a smartphone in the pocket position alone. We show that the combination of a smartwatch and a smartphone recognizes such activities with a reasonable accuracy. The recognition of such complex activities can enable well-being applications for detecting bad habits, such as smoking, missing a meal, and drinking too much coffee. We also show how to fuse information from these devices in an energy-efficient way by using low sampling rates. We make our dataset publicly available in order to make our work reproducible.