Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

Sensor-Based Activity Recognition

The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers, it is possible to determine whether a user is walking or running at a specific time of day. By correlating this knowledge with GPS data, it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.

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Preprocessing techniques for context recognition from accelerometer data

Adaptive control processes

The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing.

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Multi-sensor context-awareness in mobile devices and smart artifacts

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

Much research has been conducted that uses sensor-based modules with dedicated software to automatically distinguish the user's situation or context. The best results were obtained when powerful sensors (such as cameras or GPS systems) and/or sensor-specific algorithms (like sound analysis) were applied A somewhat new approach is to replace the one smart sensor by many simple sensors. We argue that neural networks are ideal algorithms to analyze the data coming from these sensors and describe how we came to one specific algorithm that gives good results, by giving an overview of several requirements. Finally, wearable implementations are given to show the feasibility and benefits of this approach and its implications.

S. Lowette

Papers

Real-time analysis of data from many sensors with neural networks