There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

In this work, algorithms are developed and evaluated to de- tect physical activities from data acquired using five small biaxial ac- celerometers worn simultaneously on different parts of the body. Ac- celeration data was collected from 20 subjects without researcher su- pervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. De- cision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers - thigh and wrist - the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves.

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Activity recognition from user-annotated acceleration data

How we Think

The theory of affordances

Cultivating Communities of Practice: A Guide to Managing Knowledge

In this article, we introduce and evaluate a comprehensive set of performance metrics and visualisations for continuous activity recognition (AR). We demonstrate how standard evaluation methods, often borrowed from related pattern recognition problems, fail to capture common artefacts found in continuous AR—specifically event fragmentation, event merging and timing offsets. We support our assertion with an analysis on a set of recently published AR papers. Building on an earlier initial work on the topic, we develop a frame-based visualisation and corresponding set of class-skew invariant metrics for the one class versus all evaluation. These are complemented by a new complete set of event-based metrics that allow a quick graphical representation of system performance—showing events that are correct, inserted, deleted, fragmented, merged and those which are both fragmented and merged. We evaluate the utility of our approach through comparison with standard metrics on data from three different published experiments. This shows that where event- and frame-based precision and recall lead to an ambiguous interpretation of results in some cases, the proposed metrics provide a consistently unambiguous explanation.

/pdf/performance-metrics-for-activity-recognition-5gqcshpp41.pdf

Performance metrics for activity recognition

Current research on eye-based interfaces mostly focuses on stationary settings. However, advances in mobile eye-tracking equipment and automated eye-movement analysis now allow for investigating eye movements during natural behavior and promise to bring eye-based interaction into people's everyday lives. Recent developments in mobile eye tracking equipment point the way toward unobtrusive human-computer interfaces that will become pervasively usable in everyday life. The potential applications for the further capability to track and analyze eye movements anywhere and anytime calls for new research to develop and understand eye-based interaction in mobile daily life settings.

Toward Mobile Eye-Based Human-Computer Interaction

Most Web applications are still developed ad hoc. One reason is the gap between established software design concepts and the low-level Web implementation model. We summarize work on WebComposition, a model for Web application development, then introduce the WebComposition Markup Language, an XML-based language that implements the model. WCML embodies object-oriented principles such as modularity, abstraction and encapsulation.

Object-oriented Web application development

If a mobile computing device knows how it is positioned and oriented in relation to other devices nearby, then it can provide enhanced support for multi-device and multi-user interactions. Existing systems that provide position information to mobile computers are reliant on externally deployed infrastructure, such as beacons or sensors in the environment. We introduce the Relate system, which provides fine-grained relative position information to co-located devices on the basis of peer-to-peer sensing, thus overcoming dependence on any external infrastructure. The system is realised as a hardware/software plug-in, using ultrasound for peer-to-peer sensing, USB to interface with standard mobile devices, and data abstraction and inferencing to map sensor data to a spatial model that maintains both quantitative and qualitative relationships. We present a set of services and applications to demonstrate the utility of the system. We report experimental results on the accuracy of the relative position and orientation estimates, and other aspects of system performance.

/pdf/a-relative-positioning-system-for-co-located-mobile-devices-3qhngve1a5.pdf

A relative positioning system for co-located mobile devices

Personal fabrication machines, such as 3D printers and laser cutters, are becoming increasingly ubiquitous. However, designing objects for fabrication still requires 3D modeling skills, thereby rendering such technologies inaccessible to a wide user-group. In this paper, we introduce MixFab, a mixed-reality environment for personal fabrication that lowers the barrier for users to engage in personal fabrication. Users design objects in an immersive augmented reality environment, interact with virtual objects in a direct gestural manner and can introduce existing physical objects effortlessly into their designs. We describe the design and implementation of MixFab, a user-defined gesture study that informed this design, show artifacts designed with the system and describe a user study evaluating the system's prototype.

/pdf/mixfab-a-mixed-reality-environment-for-personal-fabrication-uc0kj07eb8.pdf

Hans Gellersen

Papers

Performance metrics for activity recognition

Toward Mobile Eye-Based Human-Computer Interaction

Object-oriented Web application development

A relative positioning system for co-located mobile devices

MixFab: a mixed-reality environment for personal fabrication