Pervasive Technologies Related to Assistive Environments 

About: Pervasive Technologies Related to Assistive Environments is an academic conference. The conference publishes majorly in the area(s): Context (language use) & Robot. Over the lifetime, 1165 publications have been published by the conference receiving 11628 citations.

Creating and benchmarking a new dataset for physical activity monitoring

Abstract: Physical activity monitoring has recently become an important field in wearable computing research. However, there is a lack of a commonly used, standard dataset and established benchmarking problems. In this work, a new dataset for physical activity monitoring --- recorded from 9 subjects, wearing 3 inertial measurement units and a heart rate monitor, and performing 18 different activities --- is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a standard data processing chain and 5 different classifiers. The benchmark shows the difficulty of the classification tasks and exposes some challenges, defined by e.g. a high number of activities and personalization.

Misco: a MapReduce framework for mobile systems

Abstract: The proliferation of increasingly powerful, ubiquitous mobile devices has created a new and powerful sensing and computational environment. Software development and application deployment in such distributed mobile settings is especially challenging due to issues of failures, concurrency, and lack of easy programming models. We present a framework which provides a powerful software abstraction that hides many of such complexities from the application developer. We design and implement a mobile MapReduce framework targeted at any device which supports Python and network connectivity. We have implemented our system on a testbed of Nokia N95 8GB smartphones and demonstrated the feasibility and performance of our approach.

Ambient kitchen: designing situated services using a high fidelity prototyping environment

Abstract: The Ambient Kitchen is a high fidelity prototype for exploring the design of pervasive computing algorithms and applications for everyday environments. The environment integrates data projectors, cameras, RFID tags and readers, object mounted accelerometers, and under-floor pressure sensing using a combination of wired and wireless networks. The Ambient Kitchen is a lab-based replication of a real kitchen where careful design has hidden the additional technology, and allows both the evaluation of pervasive computing prototypes and the simultaneous capture of the multiple synchronized streams of sensor data. Previous work exploring the requirements for situated support for people with cognitive impairments motivated the design of the physical and technical infrastructure and we describe both our motivations and previous work on interaction design in kitchen environments. Finally, we describe how our lab-based prototype has been put to use as: a design tool for designers; a design tool for users; an observatory to collect sensor data for activity recognition algorithm development, and an evaluation test bed. The limitations and advantages of lab-based, as opposed to in situ home-based testing, are discussed

A survey on vision-based fall detection

Abstract: Falls are a major cause of fatal injury for the elderly population. To improve the quality of living for seniors, a wide range of monitoring systems with fall detection functionality have been proposed over recent years. This article is a survey of systems and algorithms which aim at automatically detecting cases where a human falls and may have been injured. Existing fall detection methods can be categorized as using sensors, or being exclusively vision-based. This literature review focuses on vision-based methods.

Comparing Conventional and Augmented Reality Instructions for Manual Assembly Tasks

Abstract: Augmented Reality (AR) gains increased attention as a means to provide assistance for different human activities. Hereby the suitability of AR does not only depend on the respective task, but also to a high degree on the respective device. In a standardized assembly task, we tested AR-based in-situ assistance against conventional pictorial instructions using a smartphone, Microsoft HoloLens and Epson Moverio BT-200 smart glasses as well as paper-based instructions. Participants solved the task fastest using the paper instructions, but made less errors with AR assistance on the Microsoft HoloLens smart glasses than with any other system. Methodically we propose operational definitions of time segments and other optimizations for standardized benchmarking of AR assembly instructions.