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.

/pdf/activity-recognition-from-user-annotated-acceleration-data-lh3b5ajkau.pdf

Activity recognition from user-annotated acceleration data

How we Think

The theory of affordances

Cultivating Communities of Practice: A Guide to Managing Knowledge

Wearable eye tracking for mental health monitoring

Eye gaze is a compelling interaction modality but requires user calibration before interaction can commence. State of the art procedures require the user to fixate on a succession of calibration markers, a task that is often experienced as difficult and tedious. We present pursuit calibration, a novel approach that, unlike existing methods, is able to detect the user's attention to a calibration target. This is achieved by using moving targets, and correlation of eye movement and target trajectory, implicitly exploiting smooth pursuit eye movement. Data for calibration is then only sampled when the user is attending to the target. Because of its ability to detect user attention, pursuit calibration can be performed implicitly, which enables more flexible designs of the calibration task. We demonstrate this in application examples and user studies, and show that pursuit calibration is tolerant to interruption, can blend naturally with applications and is able to calibrate users without their awareness.

Pursuit calibration: making gaze calibration less tedious and more flexible

In this work we analyse the eye movements of people in transit in an everyday environment using a wearable electrooculographic (EOG) system. We compare three approaches for continuous recognition of reading activities: a string matching algorithm which exploits typical characteristics of reading signals, such as saccades and fixations; and two variants of Hidden Markov Models (HMMs) - mixed Gaussian and discrete. The recognition algorithms are evaluated in an experiment performed with eight subjects reading freely chosen text without pictures while sitting at a desk, standing, walking indoors and outdoors, and riding a tram. A total dataset of roughly 6 hours was collected with reading activity accounting for about half of the time. We were able to detect reading activities over all subjects with a top recognition rate of 80.2% (71.0% recall, 11.6% false positives) using string matching. We show that EOG is a potentially robust technique for reading recognition across a number of typical daily situations.

/pdf/robust-recognition-of-reading-activity-in-transit-using-4ypanq2015.pdf

Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography

Natural forms of interaction have evolved for personal devices that we carry with us (mobiles) as well as for shared interactive displays around us (surfaces) but interaction across the two remains cumbersome in practice. We propose a novel cross-device interaction style for mobiles and surfaces that uses the mobile for tangible input on the surface in a stylus-like fashion. Building on the direct manipulation that we can perform on either device, it facilitates fluid and seamless interaction spanning across device boundaries. We provide a characterization of the combined interaction style in terms of input, output, and contextual attributes, and demonstrate its versatility by implementation of a range of novel interaction techniques for mobile devices on interactive surfaces.

/pdf/a-cross-device-interaction-style-for-mobiles-and-surfaces-5dpss35wkr.pdf

A cross-device interaction style for mobiles and surfaces

Research on activity recognition has traditionally focused on discriminating between different activities, i.e. to predict which activity was performed at a specific point in time. The quality of executing an activity, the how (well), has only received little attention so far, even though it potentially provides useful information for a large variety of applications. In this work we define quality of execution and investigate three aspects that pertain to qualitative activity recognition: specifying correct execution, detecting execution mistakes, providing feedback on the to the user. We illustrate our approach on the example problem of qualitatively assessing and providing feedback on weight lifting exercises. In two user studies we try out a sensor- and a model-based approach to qualitative activity recognition. Our results underline the potential of model-based assessment and the positive impact of real-time user feedback on the quality of execution.

Hans Gellersen

Papers

Wearable eye tracking for mental health monitoring

Pursuit calibration: making gaze calibration less tedious and more flexible

Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography

A cross-device interaction style for mobiles and surfaces

Qualitative activity recognition of weight lifting exercises