Wearable Camera Research Articles

Identifying predictors of personal exposure to air temperature in peri-urban India

Characterizing personal exposure to air temperature is critical to understanding exposure measurement error in epidemiologic studies using fixed-site exposure data and to identify strategies to protect public health. To date, no study evaluating personal air temperature in the general population has been conducted in a low-and-middle income country.We used data from the CHAI study consisting of 50 adults monitored in up to six non-consecutive 24 h sessions in peri-urban south India. We quantified the agreement and association between fixed-site ambient and personal air temperature, and identified predictors of personal air temperature based on housing assessment, self-reported, GPS, remote sensing, and wearable camera data.Mean (SD) daytime (6 am–10 pm) average personal air temperature was 31.2 (2.6) °C and mean nighttime (10 pm–6 am) average temperature was 28.8 (2.8) °C. Agreement between average personal air and fixed-site ambient temperatures was limited, especially at night when personal air temperatures were underestimated by fixed-site temperatures (MBE = −5.6 °C). The proportion of average personal nighttime temperature variability explained by ambient fixed-site temperatures was moderate (R2mar = 0.39); daytime associations were stronger for women (R2mar = 0.51) than for men (R2mar = 0.3). Other predictors of average nighttime personal air temperature included residential altitude, ceiling height, and household income. Predictors of average daytime personal air temperature included roof materials, GPS-tracked altitude, time working in agriculture (for women), and time travelling (for men). No biomass cooking, urban heat island, or greenspace effects were identified.R2mar between ambient fixed-site and personal air temperature indicate that ambient fixed-site temperature is only a moderately useful proxy of personal air temperature in the context of peri-urban India. Our findings suggest that people living in houses at lower altitude, with lower ceiling height and asbestos roofing sheets might be more vulnerable to heat. We also identified households with higher income, women working in agriculture and men with long commutes as disproportionately exposed to high temperatures.

Three Notes on Visual Pedagogies in Childhood Research

This article focuses on the process of creating still and video images with children through a practice referred to as ‘mashing’. The use of wearable cameras (by both researcher and children) is further explored, putting into question how documentation and visual analysis are conducted in school settings. Three concepts (i.e., gesture, encounter, endurance) are employed in relation to issues of visual representation in childhood research in order to support an understanding of documentation as pedagogically necessary in educational contexts.

Autonomous Human Activity Classification From Wearable Multi-Modal Sensors

There has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. There has been relatively less work using wearable cameras, providing first-person or egocentric view, and even fewer approaches combining egocentric video with IMU data. Using only IMU data limits the variety and complexity of the activities that can be detected. For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is. To perform fine-grained activity classification, and to distinguish between activities that cannot be differentiated by only IMU data, we present an autonomous and robust method using data from both wearable cameras and IMUs. In contrast to convolutional neural network-based approaches, we propose to employ capsule networks to obtain features from egocentric video data. Moreover, Convolutional Long Short Term Memory framework is employed both on egocentric videos and IMU data to capture the temporal aspect of actions. We also propose a genetic algorithm-based approach to autonomously and systematically set various network parameters, rather than using manual settings. Experiments have been conducted to perform 9- and 26-label activity classification, and the proposed method, using autonomously set network parameters, has provided very promising results, achieving overall accuracies of 86.6% and 77.2%, respectively. The proposed approach, combining both modalities, also provides increased accuracy compared to using only egovision data and only IMU data.

"What? That's for Old People, that." Home Adaptations, Ageing and Stigmatisation: A Qualitative Inquiry.

Older people, even those living with long-term conditions or poor mobility, can be supported to live well at home, through adapting their home to meet changing need. Installing home adaptations, from grab rails to walk in shower rooms, is cost effective, may prevent falls, reduce social isolation and improve self confidence. Despite austerity cuts to public spending, the UK government increased home adaptations’ funding. However, not much is known about older people’s experiences and understanding of acquiring and living with home adaptations and uptake of home adaptations could be improved. Using wearable camera and face to face interview data, this qualitative study explored a diverse group of older people’s retrospective experiences (n = 30). Focus group discussions were also carried out with a wide range of professionals involved in the provision of home adaptations (n = 39). Findings suggest people may delay having adaptations, because of perceived stigmatising associations with decline and vulnerability. As delaying the installation of home adaptations until crisis point is known to reduce their effectiveness, such associations need to be challenged.

Wearable Egocentric Camera as a Monitoring Tool of Free-Living Cigarette Smoking: A Feasibility Study.

Wearable sensors may be used for the assessment of behavioral manifestations of cigarette smoking under natural conditions. This paper introduces a new camera-based sensor system to monitor smoking behavior. The goals of this study were (1) identification of the best position of sensor placement on the body and (2) feasibility evaluation of the sensor as a free-living smoking-monitoring tool. A sensor system was developed with a 5MP camera that captured images every second for continuously up to 26 hours. Five on-body locations were tested for the selection of sensor placement. A feasibility study was then performed on 10 smokers to monitor full-day smoking under free-living conditions. Captured images were manually annotated to obtain behavioral metrics of smoking including smoking frequency, smoking environment, and puffs per cigarette. The smoking environment and puff counts captured by the camera were compared with self-reported smoking. A camera located on the eyeglass temple produced the maximum number of images of smoking and the minimal number of blurry or overexposed images (53.9%, 4.19%, and 0.93% of total captured, respectively). During free-living conditions, 286,245 images were captured with a mean (±standard deviation) duration of sensor wear of 647(±74) minutes/participant. Image annotation identified consumption of 5(±2.3) cigarettes/participant, 3.1(±1.1) cigarettes/participant indoors, 1.9(±0.9) cigarettes/participant outdoors, and 9.02(±2.5) puffs/cigarette. Statistical tests found significant differences between manual annotations and self-reported smoking environment or puff counts. A wearable camera-based sensor may facilitate objective monitoring of cigarette smoking, categorization of smoking environments, and identification of behavioral metrics of smoking in free-living conditions. The proposed camera-based sensor system can be employed to examine cigarette smoking under free-living conditions. Smokers may accept this unobtrusive sensor for extended wear, as the sensor would not restrict the natural pattern of smoking or daily activities, nor would it require any active participation from a person except wearing it. Critical metrics of smoking behavior, such as the smoking environment and puff counts obtained from this sensor, may generate important information for smoking interventions.

A cross-sectional study exploring levels of physical activity and motivators and barriers towards physical activity in haemodialysis patients to inform intervention development

Purpose To describe physical activity (PA) levels and motivators and barriers to PA amongst haemodialysis (HD) patients and to identify an appropriate approach to increasing their PA. Methods A cross-sectional mixed methods study conducted in a tertiary and satellite HD unit. One hundred and one participants aged 18 years and over, receiving regular HD for at least four months, were recruited. Patients with recent hospital admission or acute cardiac event were excluded. Participants completed health status (EQ-5D-3L™) and activity (Human Activity Profile (HAP)) questionnaires. A subgroup was invited to wear accelerometers and wearable cameras to measure PA levels and capture PA episodes, to inform subsequent semi-structured interviews on motivators and barriers. Semi-structured interviews were analysed using the framework method informed by constructs of the Health Belief Model. Results 98/101 completed the study (66 males, 32 females). For 68/98 participants, adjusted activity scores from the HAP indicated “impaired” levels of PA; for 67/98 participants, the EQ-5D-3L indicated problems with mobility. Semi-structured interviews identified general (fear of falls, pain) and disease specific barriers (fatigue) to PA. Motivators included tailored exercise programmes and educational support from health care professionals. Conclusions Participants indicated a need for co-development with healthcare professionals of differentiated, targeted exercise interventions. Implications for rehabilitation Healthcare professionals should encourage and motivate haemodialysis patients to participate in physical activity (PA). As part of this approach, there is a need to increase patient knowledge of safe beneficial exercise activities and help individuals identify and overcome barriers. To allow for individualised approaches, clinical interventions should focus on other community activities that patients can do outside the dialysis clinic setting and utilise existing networks such as the British Renal Society Rehabilitation Network. The dialysis clinic provides professionals the opportunity to monitor and motivate patients. Relevant education is needed for staff about the benefits of PA and how to engage patients and their carers in safe and effective approaches.

The effect of mild sleep deprivation on diet and eating behaviour in children: protocol for the Daily Rest, Eating, and Activity Monitoring (DREAM) randomized cross-over trial

BackgroundAlthough insufficient sleep has emerged as a strong, independent risk factor for obesity in children, the mechanisms by which insufficient sleep leads to weight gain are uncertain. Observational research suggests that being tired influences what children eat more than how active they are, but only experimental research can determine causality. Few experimental studies have been undertaken to determine how reductions in sleep duration might affect indices of energy balance in children including food choice, appetite regulation, and sedentary time. The primary aim of this study is to objectively determine whether mild sleep deprivation increases energy intake in the absence of hunger.MethodsThe Daily, Rest, Eating, and Activity Monitoring (DREAM) study is a randomized controlled trial investigating how mild sleep deprivation influences eating behaviour and activity patterns in children using a counterbalanced, cross-over design. One hundred and ten children aged 8–12 years, with normal reported sleep duration of 8–11 h per night will undergo 2 weeks of sleep manipulation; seven nights of sleep restriction by going to bed 1 hr later than usual, and seven nights of sleep extension going to bed 1 hr earlier than usual, separated by a washout week. During each experimental week, 24-h movement behaviours (sleep, physical activity, sedentary behaviour) will be measured via actigraphy; dietary intake and context of eating by multiple 24-h recalls and wearable camera images; and eating behaviours via objective and subjective methods. At the end of each experimental week a feeding experiment will determine energy intake from eating in the absence of hunger. Differences between sleep conditions will be determined to estimate the effects of reducing sleep duration by 1–2 h per night.DiscussionDetermining how insufficient sleep predisposes children to weight gain should provide much-needed information for improving interventions for the effective prevention of obesity, thereby decreasing long-term morbidity and healthcare burden.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12618001671257. Registered 10 October 2018.

Wearable Technology in Spine Surgery.

Although patient-reported outcome measures (PROMs) provide valuable insight into the effectiveness of spine surgery, there still remain limitations on measuring outcomes in this manner. Among other deficiencies, PROMs do not always correlate with more objective measures of surgery success. Wearable technology, such as pedometers, tri-axis accelerometer, or wearable cameras, may allow physicians to track patient progress following spine surgery more objectively. Recently, there has been an emphasis on using wearable devices to measure physical activity and limb and spine function. Wearable devices could play an important role as a supplement to PROMs, although they might have to be substantiated through adequate controlled studies to identify normative data for patients presenting with common spine disorders. This review will detail the current state of wearable technology applications in spine surgery and its direction as its utilization expands.

A Fast and Low-Cost Repetitive Movement Pattern Indicator for Massive Dementia Screening

Because of the worldwide aging population, more and more elders suffer from dementia problem. Nowadays, it is an inconvenient and time-consuming process for medical doctors to diagnose elders who live independently with possible dementia because the process imposes a large quantity of diagnostic questions from a checklist that needs to be answered by elders themselves or their caregivers either directly or after a long-term observation. In order to help doctors to make this diagnostic process easier, this article proposes a supporting system that can quickly estimate the likelihood for an elder of having dementia based on 2 to 4 hours monitoring of a behavioral test done by the elder. During the test, the elder only needs to perform certain activities selected from the so-called instrumental activities of daily living (IADL) in a smart home environment, and their movement trajectories will be extracted from motion sensors deployed in the smart home environment and be analyzed to find a potential correlation with the indoor wandering patterns. A machine learning algorithm is selected to carry out the classification task, namely, into dementia and nondementia groups, based on our proposed features of the aforementioned wandering patterns. Two data sets are employed for performance evaluation, where the first one is 232 elders including seven dementia, whereas the second one is collected by ourselves from a senior center, which is 30 elders including nine dementia. It turns out that the average precision and recall for the first data set are both up to 98.3% with area under the ROC curve (AUC-ROC) being 0.846, and those for the second data set are 89.9% and 90.0% with AUC-ROC being 0.921. Note to Practitioners —We proposed a supporting system which can classify the elders as either dementia or nondementia with high accuracy. The trajectories of the elders will be extracted from motion sensors that deployed in the smart home environment. The indoor wandering patterns according to repetitive movements are analyzed and classified using the machine learning technique. The proposed system used ambient sensors instead of wearable sensors or cameras to let the elders feel more comfortable when they are being monitored. In addition, the proposed system only required a short period of time to screen the elders and easier for medical doctors to diagnose the elders without wasting time for asking the large quantity of diagnostic questions from a checklist that needs to be answered by the elders themselves or their caregivers.

From a Lived Event to Its Autobiographical Memory: An Ecological Study Using Wearable Camera in Schizophrenia.

Cognitive disorders are considered as a core symptom of schizophrenia. Importantly, episodic autobiographical memory deficits are strongly related to patients’ social dysfunction. Although the cognitive mechanisms underlying autobiographical memory deficit are highly important to open the door for specific cognitive remediation, they are yet to be understood. The present study focused on event segmentation to check to which extent possible impairments in temporal ordering and segmenting in patients hinder memories construction. Twenty-seven patients with schizophrenia and 27 matched controls took part in an outdoor circuit while wearing a wearable camera. A week later, their memory and the temporal organization of this event have been assessed. Results showed that patients, compared with control participants, reported a reduced amount of details, especially less actions with interaction related to the event. Contrary to our initial hypotheses, event segmentation abilities in patients were not affected. The relationship between event segmentation and memory is discussed.

297Seeing is believing: the feasibility and acceptability of using wearable cameras to enhance self-management of heart failure

Abstract Introduction For people with heart failure (HF), self-management (e.g., adhering to prescribed medication, management of fluid restrictions and daily weighing) and dietary management is critical for disease management. The rise of technologies (mobile phones, wearable cameras) for healthcare use offers potential support for people to better manage their disease. We aimed to test the feasibility and utility of wearable cameras for identifying self-management practices and to determine if these images can be used to enhance self-management in people with HF. Methods Participants wore the “narrative clip”, a small wearable camera for one month during waking hours; still images were taken every 30 seconds. Using state-of-the-art artificial intelligence techniques, we investigated automated image analysis of daily life activities to determine the potential of these systems to identify four categories of human activities: medication management, dietary intake, meal preparation and physical activity. Participants also completed a semi-structured questionnaire about acceptability and feasibility. Results 30 participants (mean age 73.6 years, 60% male) with HF NYHA Class II-III were recruited. A total of 629,603 images were available for analysis. Higher order analyses were conducted to determine precision of identifying correct images for the pre-defined self-management categories. Precision of identifying the correct images was highest in dietary intake (average 49%, range 13–94%) followed by meal preparation (average 40%, range 1–99%) and physical activity (average 31%, range 0–95%). Medication management had the lowest precision (average 6%, range 6–22%). Manual review of images revealed substantial periods of sedentary time, typically paired with screen time (watching television, playing cards on a computer). All participants agreed the camera was easy to use and felt they had privacy when using the camera. The majority of participants felt comfortable wearing the camera (93%) and thought this technique will help people with HF in the future (93%). Conclusions Images from wearable cameras provided rich contextual data to better understand the lived experiences of people with HF and the device was acceptable to participants. Automated machine learning tools require more annotated data for training to enhance precision, which will be achieved via further annotation, fine-tuning and retraining the data analysis model. Despite these challenges, the data collected can be used as an adjunct to traditional data collection methods such as self-report. Once data analysis techniques are refined, objective data from wearable cameras may also prove useful for nurses to provide tailored education for self-management. Acknowledgement/Funding Heart Foundation Vanguard Grant, Australia (101348)

Adapting old methods for new trends

PDS 65: Exposure assessment: implications for epidemiology, Exhibition Hall (PDS), Ground floor, August 27, 2019, 1:30 PM - 3:00 PM Background: There are an estimated two billion informal sector workers around the globe. There is a clear need to adapt existing methods for identifying occupational hazards to meet the needs of unregulated and unpredictable work environments where unprotected workers face substantial risk from numerous hazards. We designed and applied a method of deriving time-activity patterns using wearable cameras to characterize time-use and contemporaneous, continuous measures of personal inhalation exposure to size-specific particulate matter (PM) among workers at an informal electronic-waste (e-waste) recovery site. Methods: 142 workers at the Agbogbloshie e-waste site in Ghana wore a sampling backpack with a wearable camera and real-time particle monitor during 171 shifts. Images and PM2.5 measurements were collected each minute, along with self-reported recall of time-activity (30-minute resolution) using a diary. Collected images (N=35,588) were processed to identify activities using criteria established through worker interviews, observation and existing literature. Descriptive statistics were generated for activity types, frequency and, as proof of concept, to demonstrate the camera’s ability to detect predicted relationships between PM2.5 and e-waste tasks. A kappa statistic measured agreement between self-reported and image-based time-activity data. Results: Based on image-based time-activity patterns, workers primarily dismantle, sort/load, burn and transport e-waste materials for metal recovery with high variability in activity duration. The overall geometric mean (GM) of personal PM2.5 on-site was 60.1 µg/m3 (SD: 2.1); PM2.5 was highest during burning, sorting/loading, and dismantling (GM: 90.4, 67.6, and 67.3 µg/m3 respectively). However, PM2.5 during long periods of non-work-related activities exceeded the World Health Organization 24-hour ambient PM2.5 guideline 75% of the time. Image-derived and self-reported time-activity had poor agreement (kappa = 0.17). Conclusions: Even in complex, non-formalized work environments, wearable cameras can improve occupational exposure assessments and identify activities associated with high exposures to workplace hazards by providing a transparent source of highly resolved time-activity data.

Woman at work and other untold stories from 35,000 wearable camera images taken on an informal electronic-waste recovery site

GREEN, AIR, LMIC: PECHAKUCHA COMES TO ISEE! Johan Friso Foyer, Floor 1, August 27, 2019, 10:30 AM - 12:00 PM As part of my doctoral research, I have viewed 35,588 time-lapse images taken by a wearable camera worn by electronic-waste (e-waste) recovery workers at an informal worksite in Ghana. Each consecutive image captures an instant of life on the job. The images were collected as an alternative to standard pen and paper time-activity diaries. Despite the effort to process the images, they proved to be a highly precise and transparent source of time-activity data. When combined with contemporaneous and continuous measures of personal exposure to size-specific particulate matter (PM), they allowed us to describe associations between specific job tasks and personal PM exposure. We observed several predicted associations, but it is the unpredicted patterns captured by images that I want to share in this presentation. For example, our study is comprised of 100 male e-waste workers, however, it was the colorfully dressed women on the worksite that kept catching my eye. Not only were they on-site selling food and water, but they were most commonly standing near the male workers while they burned e-waste in open, surface fires. Among all the jobs captured in our data, burning e-waste is associated with the highest overall levels of PM2.5 exposure (Geometric Mean: 90 µg/m3). Why was this vulnerable population lingering near the men putting e-waste in and out of the fire and why were they not included in our study? One image captured a woman pouring water on copper wires that had recently been pulled from the fire. In low-and-middle-income countries, there are more women in informal employment than men. This presentation is, most importantly, to honor these female workers who are not invisible. Note: The images to be used in this Pechakucha presentation were collected with the permission of all study participants. No images with identifiable visual information will be used.

WristCam: A Wearable Sensor for Hand Trajectory Gesture Recognition and Intelligent Human–Robot Interaction

As a promising component for body sensor networks, the wearable sensors for hand gesture recognition have increasingly received great attention in recent years. By interpreting human intentions through hand gestures, the natural human–robot interaction can be realized in the smart home where the youth and the elderly can perform hand gestures to control the household robot or the robotic wheelchair. Here, a wearable wrist-worn camera sensor was shown to recognize hand trajectory gestures. The moving velocity of the user’s hand was deduced from the matched speeded up robust features keypoints of the moving background of the video sequence. Furthermore, the segmentation of continuous gestures was achieved by detecting the predefined gesture starting signal from the hand region of the image, which was segmented by the lazy snapping algorithm. In this paper, 10 types of gestures and 1350 gesture samples collected from 15 subjects at three different scenes were classified by the dynamic time warping algorithm and the results achieved an average recognition accuracy up to 97.6%. Moreover, the practicability of the proposed system was further demonstrated by controlling a cooperative robot to draw letters on paper.

Egocentric visitors localization in natural sites

Localizing visitors in natural environments is challenging due to the unavailability of pre-installed cameras or other infrastructure such as WiFi networks. We propose to perform localization using egocentric images collected from the visitor’s point of view with a wearable camera. Localization can be useful to provide services to both the visitors (e.g., showing where they are or what to see next) and to the site manager (e.g., to understand what the visitors pay more attention to and what they miss during their visits). We collected and publicly released a dataset of egocentric videos asking 12 subjects to freely visit a natural site. Along with video, we collected GPS locations by means of a smartphone. Experiments comparing localization methods based on GPS and images highlight that image-based localization is much more reliable in the considered domain and small improvements can be achieved by combining GPS- and image-based predictions using late fusion.

Radar sensing for healthcare

Although traditionally associated with defence and security domains, radar sensing has attracted significant interest in recent years in healthcare applications. These include the monitoring of vital signs such as respiration, heartbeat, and blood pressure, analysis of gait and mobility levels, classification of human activities to promptly detect critical events such as falls, as well as the evaluation of fitness and reactivity levels. The attractiveness of radar against alternative technologies such as wearable sensors or cameras lies in its contactless capabilities, whereby people do not need to wear, carry, or interact with any additional device, and plain images of people and private environments are not recorded. In this letter, we discuss some of the most recent achievements and outstanding research challenges related to radar applications in healthcare and present some results from our work at the University of Glasgow, including a dataset of radar signatures of human activities that are openly shared with the wider community.

Food store environment examination - FoodSee: a new method to study the food store environment using wearable cameras.

Food environments shape food behaviours and are implicated in rising rates of obesity worldwide. Measurement of people's interactions with food stores is important to advance understanding of the associations between the food environment and in-store behaviour. This paper describes a new method, Food Store Environment Examination (FoodSee) to measure people's interaction with the food store environment in a feasibility study focused on convenience stores and children. One hundred and sixty-eight randomly selected children (aged 11-13 years) recruited from 16 randomly selected schools in Wellington, New Zealand, used wearable cameras for 4 days that recorded images every 7 s. The study was conducted from July 2014 to June 2015. All images of convenience stores and service stations, and a sample of images from supermarkets, were evaluated to determine the feasibility of assessing food availability and marketing. The outcomes of interest assessed were: food product availability, placement, packaging, branding, price promotion, purchases and consumption. Thirty-seven children (22%) visited a convenience store or service station at least once during the study period. In total, there were 65 visits to 34 different stores. Seven hundred and nineteen images revealed the in-store environment. Of those, 86.1% were usable and able to be analysed for the outcomes of interest. The FoodSee methodology provides a promising new method to study people's interaction with the in-store food environment. The evidence generated will be valuable in understanding and improving the food store environment within which people shop, and will contribute to efforts to address obesity globally.

Space-time analysis of unhealthy food advertising: New Zealand children's exposure and health policy options.

Reducing children's exposure to unhealthy food advertising is an accepted strategy to end childhood obesity. This study aimed to (i) measure children's space-time exposures to unhealthy food advertising in public outdoor spaces, using GPS and wearable cameras; and (ii) test effectiveness of banning options. We compiled data (collected July 2014-June 2015) on 138 12-year-old children in Wellington, New Zealand, using wearable cameras and GPS devices worn over 4 days. In 2017-18, we linked 59150 images taken in public outdoor spaces to GPS data. Of these, 1631 contained unhealthy food advertising exposures, defined as ≥50% of an advertisement observed in each image. We examined spatial patterns using kernel density mapping and graphed space-time trends. We interpolated a kriged exposure rate across Wellington to estimate exposure reductions for potential bans. Children were exposed to 7.4 (95% CI 7.0-7.8) unhealthy food advertisements/hour spent in outdoor public spaces. Exposures occurred in shopping centres, residential areas and sports facilities, commonly involving fast food, sugary drinks and ice cream. Peak exposure times were weekend afternoons/evenings and weekdays before/after school. In Wellington, we estimated that banning such advertising within 400 m of playgrounds would yield a 33% reduction in exposure, followed by in residential areas (27%), within 400 m of schools (25%), and 50% for a ban combining all above. This work documents the extent of children's exposure to unhealthy food advertising and the potential impact of bans. Given the ubiquity of advertising in public spaces, this New Zealand research offers innovative methods and findings likely relevant in other jurisdictions.

The hidden impact of home adaptations: Using a wearable camera to explore lived experiences and taken-for-granted behaviours.

Most adults over 65years old live in mainstream housing in the United Kingdom, yet these can often be unsuitable for an individual's needs. With increased understanding of the relationship between housing, and health and well-being, the importance of modifying the home to suit individuals is recognised as being paramount. However, it is often difficult to monitor the ways in which home adaptations and equipment are used in the home. This study used innovative wearable technology to explore everyday, lived experiences of using home adaptations and equipment. Six older adults who had received a major home adaptation in the last 24months took part in this study. Each participant used a wearable camera for one day and participated in a semi-structured interview while watching the images back as a 'slideshow'. Using this novel approach, three themes were generated from the data: acquiring adaptations and equipment, adapting routine and changing behaviour, and inconsistent and unintended uses. The findings of this study open up the complexity of the lived experience of using home adaptations and equipment. Experiences from access to long-term outcomes are personal, and individuals modify and use the adaptations in various ways to suit their own needs. The wearable camera allowed additional insight into lived experience that would otherwise not have been captured without its use, as the photographs acted as a way of stimulating conversation and highlighting taken-for-granted behaviours not often consciously considered by the individuals.

Using Wearable Technology to Increase Police Legitimacy in Uruguay: The Case of Body-Worn Cameras

What are the effects of wearable police cameras on perceptions of the police? In this study, we report causal estimates from a crossover randomized controlled trial in Uruguay on the effects of use of body-worn cameras by traffic police on the perceptions of legitimacy and satisfaction by drivers ticketed for traffic violations. We pay particular attention to the effects on procedural justice—that is, perceptions of the fairness of the interactions between officers and drivers—without neglecting other features of legitimacy (i.e., effectiveness, distributive justice, and lawfulness). With the exception of lawfulness, wearable surveillance apparatuses showed improvements across all dimensions of legitimacy compared to control conditions, with medium to large effect sizes. The overall satisfaction from the police-public interaction was significantly higher when officers used body-worn cameras. These findings suggest that wearable surveillance technology can lead to enhanced perceptions of legitimacy across multiple dimensions, not just increased efficiency, as well as the perceived overall quality of police-public interaction.