Activity classification using realistic data from wearable sensors

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.     

Customizing User Interaction in Smart Phones

Emerging input modalities could facilitate more efficient user interactions with mobile devices. An end-user customization tool based on user-defined context-action rules lets users specify personal, multimodal interaction with smart phones and external appliances. The tool's input modalities include sensor-based, user-trainable free-form gestures; pointing with radio frequency tags; and implicit inputs based on such things as sensors, the Bluetooth environment, and phone platform events. The tool enables user-defined functionality through a blackboard-based context framework enhanced to manage the rule-based application control. Test results on a prototype implemented on a smart phone with real context sources show that rule-based customization helps end users efficiently customize their smart phones and use novel input modalities.     

Managing context information in mobile devices

We present a uniform mobile terminal software framework that provides systematic methods for acquiring and processing useful context information from a user's surroundings and giving it to applications. The framework simplifies the development of context-aware mobile applications by managing raw context information gained from multiple sources and enabling higher-level context abstractions.     

Collaborative context determination to support mobile terminal applications

Mobile devices, together with their users, are constantly moving from one situation to another. To adapt applications to these changing contexts, the devices must have ways to recognize the contexts. There are various sources for context information: sensors, tags, positioning systems, to name a few. The raw signals from these sources are translated into higher-level interpretations of the situation. Unfortunately, such data is often unreliable and constantly changing. We seek to improve the reliability of context recognition through an analogy to human behavior. Where multiple devices are around, they can jointly negotiate on a suitable context and behave accordingly. This approach is becoming particularly attractive with the multitude of personal devices on the market. We present a collaborative context determination scheme, suggest examples of potential applications of such collaborative behavior, and raise issues of context recognition, context communication, and network requirements.     

Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions

Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.