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Shoaib M.,Pervasive Systems Group | Bosch S.,Pervasive Systems Group | Durmaz Incel O.,Galatasaray University | Scholten H.,Pervasive Systems Group | Havinga P.J.M.,Pervasive Systems Group
Sensors (Switzerland) | Year: 2014

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. © 2014 by the authors; licensee MDPI, Basel, Switzerland.


Shoaib M.,Pervasive Systems Group | Bosch S.,Pervasive Systems Group | Incel O.D.,Galatasaray University | Scholten H.,Pervasive Systems Group | Havinga P.J.M.,Pervasive Systems Group
Sensors (Switzerland) | Year: 2016

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. © 2016 by the authors; licensee MDPI, Basel, Switzerland.


Shoaib M.,Pervasive Systems Group | Bosch S.,Pervasive Systems Group | Incel O.D.,Galatasaray University | Scholten H.,Pervasive Systems Group | Havinga P.J.M.,Pervasive Systems Group
Sensors (Switzerland) | Year: 2015

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. © 2015 by the authors; licensee MDPI, Basel, Switzerland.


Das K.,Pervasive Systems Group | Mathews E.,Pervasive Systems Group | Zand P.,Pervasive Systems Group | Ramirez A.S.,University of Twente | Havinga P.,Pervasive Systems Group
Proceedings of the IEEE International Conference on Industrial Technology | Year: 2015

Energy harvesting technologies have brought a paradigm shift in the industrial automation sector by procreating self-powered wireless input/output (I/O) devices. Unfortunately, current wireless technologies for industrial applications, such as ISA100.11a and WirelessHART, are yet far from supporting harvester powered I/O devices. Although several works have been conducted to address the requirements of energy harvested I/O devices, most of those have focused on minimizing the I/O energy consumption during the steady-state phase of the network. However, a very important aspect, the energy consumption during network joining that consumes a significant amount of energy, is overlooked in these works. In this paper, we therefore analyze the I/O energy consumption in ISA100.11a network during the joining phase in addition to that in normal operation to better understand the challenges of energy harvesting communications. Then, we propose an energy efficient network joining scheme to support harvester powered I/O devices in ISA100.11a network. The proposed scheme significantly reduces the joining delay when compared with the traditional ISA100.11a joining scheme. We also propose a reliable data transmission scheme for energy harvested I/O devices by utilizing spatial diversity that can outperform ISA100.11a data publication through significant improvement in packet reception. © 2015 IEEE.


Shoaib M.,Pervasive Systems Group | Bosch S.,Pervasive Systems Group | Incel O.D.,Galatasaray University | Scholten H.,Pervasive Systems Group | Havinga P.J.,Mahatma Jyotiba Phule Rohilkhand University
Sensors (Basel, Switzerland) | Year: 2015

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.


Shoaib M.,Pervasive Systems Group | Bosch S.,Pervasive Systems Group | Incel O.D.,Galatasaray University | Scholten H.,Pervasive Systems Group | Havinga P.J.,Mahatma Jyotiba Phule Rohilkhand University
Sensors (Basel, Switzerland) | Year: 2014

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.


PubMed | Pervasive Systems Group and Galatasaray University
Type: Journal Article | Journal: Sensors (Basel, Switzerland) | Year: 2014

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.


PubMed | Pervasive Systems Group and Galatasaray University
Type: Journal Article | Journal: Sensors (Basel, Switzerland) | Year: 2016

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.

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