The widespread presence of motion sensors on users' personal mobile devices has spawned a growing research interest in human activity recognition (HAR). However, when deployed at a large-scale, e.g., on multiple devices, the performance of a HAR system is often significantly lower than in reported research results. This is due to variations in training and test device hardware and their operating system characteristics among others. In this paper, we systematically investigate sensor-, device- and workload-specific heterogeneities using 36 smartphones and smartwatches, consisting of 13 different device models from four manufacturers. Furthermore, we conduct experiments with nine users and investigate popular feature representation and classification techniques in HAR research. Our results indicate that on-device sensor and sensor handling heterogeneities impair HAR performances significantly. Moreover, the impairments vary significantly across devices and depends on the type of recognition technique used. We systematically evaluate the effect of mobile sensing heterogeneities on HAR and propose a novel clustering-based mitigation technique suitable for large-scale deployment of HAR, where heterogeneity of devices and their usage scenarios are intrinsic.

Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition

We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.

/pdf/accelerometer-based-transportation-mode-detection-on-1gic4c2v8y.pdf

Accelerometer-based transportation mode detection on smartphones

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

https://ris.utwente.nl/ws/files/6518323/sensors-15-02059.pdf

A survey of online activity recognition using mobile phones

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

/pdf/a-survey-on-mobile-crowdsensing-systems-challenges-solutions-224oot2vez.pdf

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

https://oar.princeton.edu/bitstream/88435/pr1w49x/1/OA_MotivationalGameTheoreticApproachPeerPeerEnergyTradingSmartGrid.pdf

A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid

Design challenges in motivating change for sustainable urban mobility

With the advances in smartphone technologies, sustainable mobility has become an active research topic in the field of ubiquitous computing. We present a persuasive mobile application that automatically tracks the transportation modes and CO2 emissions of the trips of the user and utilizes this information to present a set of actionable mobility challenges to the user. A longitudinal pilot experiment with the system showed that subjects perceived the concept of challenges as positive, with constructive findings to inform further development of the application especially related to personalized challenges.

MatkaHupi: a persuasive mobile application for sustainable mobility

We introduce CoSense, a collaborative sensing platform for mobile devices that opportunistically distributes sensing tasks between familiar devices in close proximity. We use empirical energy measurements together with data collected from everyday transportation behaviour to demonstrate that our solution can significantly reduce power consumption while maintaining the best possible sensing accuracy.

CoSense: a collaborative sensing platform for mobile devices

We present Ma$$iv, an intelligent mobile grocery assistant that provides support for the customer during the entire shopping process To guide the design of Ma$$iv, we conducted a user study that explored customer preferences regarding features in a mobile grocery aid We first describe the study and its results, after which we introduce the design principles and design of Ma$$iv We also describe the features that Ma$$iv supports and discuss functionalities that we are integrating into Ma$$iv As part of the discussion, we describe technical challenges that we have encountered during our development efforts

Samuli Hemminki

Papers

Accelerometer-based transportation mode detection on smartphones

Design challenges in motivating change for sustainable urban mobility

MatkaHupi: a persuasive mobile application for sustainable mobility

CoSense: a collaborative sensing platform for mobile devices

Ma$$iv -- An Intelligent Mobile Grocery Assistant