Características generales del teatro en el exilio de Max Aub

To date, there is a lack of methods to investigate balance control behavior in complex, real- world environments. Falls have a multifactorial nature and are not originated from one intrinsic or extrinsic cause. This fact necessitates the need to develop methods for detection of associated risk factors. Most of the FRA tools neglect the extrinsic fall risks, which might explain why current clinical prediction models provide only poor to fair predictive ability [140]. The exposure to environmental hazards, such as curbs, carpets and pets, can only be investigated in daily life. Therefore, achieving contextual awareness during daily life activities may offer more promising estimates of fall risk.

Robinovich et al [111] installed digital video cameras in common areas (dining rooms, lounges, hallways) of two long-term care facilities in British Columbia, Canada and cap- tured 227 falls from 130 individuals (mean age = 78 years, SD 10). The authors did observational study between April 2007, and June 2010. When a fall occurred, facility staff completed an incident report and contacted them so that we could collect video footage. A team reviewed each fall video with a validated questionnaire that probed the cause of imbalance and activity at the time of falling. They found that the most frequent

cause of falling was incorrect weight shifting, which accounted for 41% (93 of 227) of falls, followed by trip or stumble (48, 21.41%), hit or bump (25, 11.41%), loss of support (25, 11.41%), and collapse (24, 11.41%). Slipping accounted for only 3.41% (six) of falls. The three activities associated with the highest proportion of falls were forward walking (54 of 227 falls, 24.41%), standing quietly (29 falls, 13.41%), and sitting down (28 falls, 12.41%). Compared with previous reports from the long-term care setting, they identified a higher occurrence of falls during standing and transferring, a lower occurrence during walking, and a larger proportion due to centr-of-mass perturbations than base-of-support perturbations.

The only SP-based context-aware system that we found in the literature is proposed by Menelas [139]. The authors employ a smartshoe to track the movement of patients and categorize the fall risk status of the environment, and then broad-cast this in real-time to a smart-phone application which focuses solely on reducing extrinsic risk factors. It considers the environmental conditions in which older adults function and notifies them of potential risks. The environment is scanned for slippery surfaces and steep slope by means of a smart shoe with built-in sensors. No vision-based sensor was employed in their research.

Advances in mobile vision systems and wearable egocentric cameras, i.e., GoPro and Autographer wearable cameras, enabling a new form of capturing human experience. The first-person perspective photos and videos captured by these cameras can provide rich and objective evidence of a person’s everyday activities that (in contrast to other motion capture systems) can be worn to capture contextual information in environments other that senior’s dwellings.

The results of the recent research (2015) [141,142,143] supported by Fujisto Laborato- ries, Japan, indicate that the external environment has a significant impact on the quality of gait metrics; s a result. They emphasized that context of external walking environment is an important consideration when analyzing ambulatory gait metrics from the unsuper- vised home and community setting. The intention of their research is to understand the relationship between mobility metrics obtained outside of the clinic or laboratory and the context of the external environment.

Taylor et al. (2015) [143] employed three sensors to collect gait and environmental context information. Participants (N =12, age 70.9 ± 6.63 years) were recruited from the community and a falls clinic, including a control group with no history of falling and in- dividuals who had fallen at least once in the past 6 month. The participants were studied during their daily lives for seven days. Two Shimmer3 9-dof inertial sensors(Shimmer Re- search Ltd, Dublin, Ireland) were attached via custom made semi elastic Velcro straps above the ankle to obtain gait information. An Autographer wearable camera (Autogra- pher, Cambridge, UK) was worn around the participants’ neck used to determine what type of environment was being walked in (images being recorded every 15 seconds) Gait metrics for each gait event were found, and for each identified gait event, the Autographer images corresponding to the same time period were manually reviewed to obtain contextual information. The most common annotations that were found in most of the participants were outdoors on pavement, indoors on carpet, and indoors on polished or hardwood. The results showed that both groups spent relatively little time walking in challenging environ- mental conditions, and that the fallers spent significantly less time walking under regular conditions (no effect on gait) and outdoors. Analysis of gait metrics showed that the fallers were slightly slower in general, and more noticeable differences were observed when the par- ticipants were regrouped according to mobility levels determined from baseline assessments using traditional methods.

Patterson et al. [141], used the same three-sensor system that was implemented in [143] and asked 10 healthy subjects (age 29.4 ± 4.7 years) to wear the inertial sensor on their both shanks and a wearable camera around their neck. Participants were asked to walk for thirty minute in five different conditions: 1) normal path, 2) busy hallway, 3) rough ground, 4) blind folded, and 5) on a hill. Several gait parameters such as stride time, stride time variability, stance time, and peak shank rotation rate during swing were calculated. A researcher walked alongside the participants to tell them where to go and noted the times. The authors pointed out that the algorithm that was presented by Greene et al. [148] did not work properly in detection of ICs and TOs in a gait cycle (from the sagittal plane gyroscope signal), mainly because the algorithm was developed for straight line walking; so they needed to modify the algorithm in order to detect ICs and TOs under

those five different conditions in a more natural environment. They found that stride time was considerably different between several of the conditions. Moreover, the ”peak shank rotation rate” during swing was calculated (because it is a useful variable in detecting abnormal gait patterns), which was lower in the busy, blind and hill walking conditions.

Although the aforementioned studies have attempted to detect gait abnormalities and characteristics in different environments, none of them have proposed an automated vision- based way for detection of fall-related environmental risk factors. While these research groups have recorded videos, either with egocentric cameras or ambient cameras in the se- niors’ dwellings, to laboriously identify environmental circumstances manually, the present thesis is the first to develop and evaluate an automated method to the author’s knowledge. In section 4.2 more related studies to our approach for the detection of environmental risk factors (discussed in chapter4) that integrated computer vision techniques to achieve context-awareness, specifically in robotics, are discussed.