Lyndsay Williams

SenseCam: a retrospective memory aid

This paper presents a novel ubiquitous computing device, the SenseCam, a sensor augmented wearable stills camera. SenseCam is designed to capture a digital record of the wearers day, by recording a series of images and capturing a log of sensor data. We believe that reviewing this information will help the wearer recollect aspects of earlier experiences that have subsequently been forgotten, and thereby form a powerful retrospective memory aid. In this paper we review existing work on memory aids and conclude that there is scope for an improved device. We then report on the design of SenseCam in some detail for the first time. We explain the details of a first in-depth user study of this device, a 12-month clinical trial with a patient suffering from amnesia. The results of this initial evaluation are extremely promising; periodic review of images of events recorded by SenseCam results in significant recall of those events by the patient, which was previously impossible. We end the paper with a discussion of future work, including the application of SenseCam to a wider audience, such as those with neurodegenerative conditions such as Alzheimers disease.

Passive capture and ensuing issues for a personal lifetime store

Passive capture lets people record their experiences without having to operate recording equipment, and without even having to give recording conscious thought. The advantages are increased capture, and improved participation in the event itself. However, passive capture also presents many new challenges. One key challenge is how to deal with the increased volume of media for retrieval, browsing, and organizing. This paper describes the SenseCam device, which combines a camera with a number of sensors in a pendant worn around the neck. Data from SenseCam is uploaded into a MyLifeBits repository, where a number of features, but especially correlation and relationships, are used to manage the data.

The use of a wearable camera, SenseCam, as a pictorial diary to improve autobiographical memory in a patient with limbic encephalitis: A preliminary report

This case study describes the use of a wearable camera, SenseCam, which automatically captures several hundred images per day, to aid autobiographical memory in a patient, Mrs B, with severe memory impairment following limbic encephalitis. By using SenseCam to record personally experienced events we intended that SenseCam pictures would form a pictorial diary to cue and consolidate autobiographical memories. After wearing SenseCam, Mrs B plugged the camera into a PC which uploaded the recorded images and allowed them to be viewed at speed, like watching a movie. In the control condition, a written diary was used to record and remind her of autobiographical events. After viewing SenseCam images, Mrs B was able to recall approximately 80% of recent, personally experienced events. Retention of events was maintained in the long-term, 11 months afterwards, and without viewing SenseCam images for three months. After using the written diary, Mrs B was able to remember around 49% of an event; after one month with no diary readings she had no recall of the same events. We suggest that factors relating to rehearsal/re-consolidation may have enabled SenseCam images to improve Mrs Bs autobiographical recollection.

SmartMoveX on a Graph - An Inexpensive Active Badge Tracker

Measuring the locations of people in a building is an important part of ubiquitous computing. We present new hardware and software for this purpose. The hardware, called SmartMoveX, is an active badge system in which a small radio transmitter is attached to the person being tracked. Receivers placed in the buildings existing offices, connected to existing PCs, transmit signal strength readings to a central PC using the buildings existing computer network. Combined with the low cost of the hardware, using the existing network makes this active badge system much less expensive than many others. To compute locations based on signal strength, we gathered signal strength readings from predefined location nodes in the building. We defined a graph on these nodes, which allowed us to enforce constraints on computed movements between nodes (e.g. cannot pass through walls) and to probabilistically enforce our expectations on transitions between connected nodes. Modeling the data with a hidden Markov model, we used the Viterbi algorithm to compute optimal paths based on signal strengths over the node graph. The average location error was 3.05 meters, which compared favorably to a simple nearest neighbor algorithms average location error of 4.57 meters.