CISUC

A critical review of the effects of wearable cameras on memory

Authors

Abstract

The rise of "lifelogging" in this era of rapid technological innovation has led to great interest in whether or not such technologies could be used to rehabilitate memory. Despite the growing number of studies using lifelogging, such as with wearable cameras, there is a lack of a theoretical framework to support its effective use. The present review focuses on the use of wearable cameras. We propose that wearable cameras can be particularly effective for memory rehabilitation if they can evoke more than a mere familiarity with previous stimuli, and reinstate previous thoughts, feelings and sensory information: recollection. Considering that, in memory impairment, self-initiated processes to reinstate previous encoding conditions are compromised, we invoke the environmental support hypothesis as a theoretical motivation. Twenty-five research studies were included in this review. We conclude that, despite the general acceptance of the value of wearable cameras as a memory rehabilitation technique, only a small number of studies have focused on recollection. We highlight a set of methodological issues that should be considered for future research, including sample size, control condition used, and critical measures of memory and other domains. We conclude by suggesting that research should focus on the theory-driven measure of efficacy described in this review, so that lifelogging technologies can contribute to memory rehabilitation in a meaningful and effective manner.

Journal

Neuropsychological Rehabilitation, pp. 1-25, January 2016

DOI


Cited by

Year 2017 : 1 citations

 Mair, Ali, Marie Poirier, and Martin A. Conway. "Supporting older and younger adults’ memory for recent everyday events: A prospective sampling study using SenseCam." Consciousness and Cognition 49 (2017): 190-202.

Year 2016 : 1 citations

 Kestens, Yan, Benoit Thierry, and Basile Chaix. "Re-creating daily mobility histories for health research from raw GPS tracks: Validation of a kernel-based algorithm using real-life data." Health & place 40 (2016): 29-33.