Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies.

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL-smartphones, wearables, video, and electronic components-and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard.

Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone.

Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80⁻85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64⁻74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine).

The Praise and Price of Pokémon GO: A Qualitative Study of Children's and Parents' Experiences.

BACKGROUND: Physical activity has multiple health benefits; however, the majority of children around the world do not attain the recommended levels of daily physical activity. Research has shown that the game Pokémon GO has increased the amount of physical activity of players and that the game has the potential to reach populations that traditionally have low levels of physical activity. Therefore, there is a need to understand which game components can promote initial and sustained physical activity. By using a qualitative research approach, it is possible to achieve rich descriptions and enhance a deep understanding of the components promoting physical activity among children in a game such as Pokémon GO. OBJECTIVE: The objective of this study was to explore children's and parents' experiences playing Pokémon GO. METHODS: Eight families comprising 13 children (aged 7-12 years) and 9 parents were selected using purposeful sampling. Data collected using focus groups were analyzed using qualitative latent content analysis. RESULTS: The following three themes were revealed: (1) exciting and enjoyable exploration; (2) dangers and disadvantages; and (3) cooperation conquers competition. The first centers around the present and possible future aspects of Pokémon GO that promote physical activity. The second focuses on unwanted aspects and specific threats to safety when playing the game. The third shows that cooperation and togetherness are highly valued by the participants and that competition is fun but less important. CONCLUSIONS: Components from Pokémon GO could enhance the efficacy of physical activity interventions. Cooperation and exploration are aspects of the game that preferably could be transferred into interventions aimed at promoting children's physical activity.

Analyzing body movements within the Laban Effort Framework using a single accelerometer.

This article presents a study on analyzing body movements by using a single accelerometer sensor. The investigated categories of body movements belong to the Laban Effort Framework: Strong-Light, Free-Bound and Sudden-Sustained. All body movements were represented by a set of activities used for data collection. The calculated accuracy of detecting the body movements was based on collecting data from a single wireless tri-axial accelerometer sensor. Ten healthy subjects collected data from three body locations (chest, wrist and thigh) simultaneously in order to analyze the locations comparatively. The data was then processed and analyzed using Machine Learning techniques. The wrist placement was found to be the best single location to record data for detecting Strong-Light body movements using the Random Forest classifier. The wrist placement was also the best location for classifying Bound-Free body movements using the SVM classifier. However, the data collected from the chest placement yielded the best results for detecting Sudden-Sustained body movements using the Random Forest classifier. The study shows that the choice of the accelerometer placement should depend on the targeted type of movement. In addition, the choice of the classifier when processing data should also depend on the chosen location and the target movement.

Optimal placement of accelerometers for the detection of everyday activities.

This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.

Context-aware life-logging for persons with mild dementia.

The demands of introducing technology to support independent living is increasing. This is true also for persons suffering from mild dementia who may have difficulties remembering important information, such as activities, numbers, names, objects, faces, and so on. This paper presents a context-aware life-logging system, called MemoryLane, which can support independent living and improve quality of life for persons with mild dementia. The system offers both real time support as well as possibilities to rehearse and recall activities for building episodic memory. This paper also presents a mobile client to be used in MemoryLane, as well as an evaluation of the importance of different data for the purpose of memory recollection.

The development of personalised cognitive prosthetics.

Persons suffering from mild dementia can benefit from a form of cognitive prosthetic which can be used to assist them with their day to day activities. Within our current work we are aiming to develop a successful user-validated cognitive prosthetic for persons with mild dementia. We have devised a three phased waterfall methodology to support our developments. Based on the evaluation of the first of these phases which involved the processes of user requirements gathering, prototype development and evaluation of in situ deployment of the technology we have been able to guide the technical development within the second phase of our work. Within this paper we provide an overview of the first phase of our methodology and demonstrate how we have used the results from this to guide the second phase of our work, especially with regards to the notion of personalisation.

HomeCI--a visual editor for healthcare professionals in the design of home based care.

The demands of introducing a more practical means of managing and monitoring technology within the home environment to support independent living are increasing. Within this paper we present a prototype solution, referred to as HomeCI, which allows healthcare professionals to establish the conditions/rules within which technology in the home should operate. The HomeCI concept is based on the use of visual notation and has been designed for use by healthcare professionals with a non technical background. Within the paper we present the design of the first version of the HomeCI visual editor and present the results of a usability study conducted on 4 healthcare professionals.