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Of all the injuries resulting from falls, hip fractures are often the most stressful and produce high levels of disability. The costs of hip fracture for Older Adults are considerable. These include risks of mortality, immediate morbidity associated with surgery, the loss of independence and long term deterioration in levels of functioning. In addition to any loss of function related to physical trauma, psychological trauma i.e. fear of falling, may also produce a decline in levels of physical and social activity which is self imposed. This study investigated the efficacy of a brief cognitive intervention in improving outcome amongst older adults after hip fracture. Two groups of participants were investigated with one group receiving the cognitive intervention and the other group treatment as usual. No significant effect was found in reducing fear of falling. However significant differences were found post-study in cognitive function, instrumental activities and duration of hospital stay suggesting that the intervention was effective in enhancing the outcome of rehabilitation. These results are discussed in relation to self-efficacy theory.

Class of 2011 Abstract / OBJECTIVES: To design and evaluate a dosing schedule pictogram in an elderly population. To identify possible reasons for misunderstanding of a dosing schedule pictogram and, if appropriate, redesign the pictogram to reflect reasons for misunderstanding. METHODS: Through a modified focus group analysis involving residents of a retirement community in Tucson, Arizona, the understandability of a morning-noon-night color pictogram was assessed. Residents’ interpretation of the pictogram as correct or incorrect was assessed using a decision-tree analysis. The percentage of correct responses was calculated and compared to established pictogram criteria for acceptability. Lastly, potential reasons for misunderstanding were assessed. RESULTS: Thirteen residents, six males and seven females, participated in the study. The rate of correct interpretation was 84.6%, which nearly satisfies the ANSI criterion of 85% correct and far exceeds the ISO criterion of 67% correct. Two participants incorrectly interpreted the pictogram, but their comments, however, indicated good comprehension of time of day. CONCLUSION: The dosing schedule pictogram was correctly interpreted by a majority of study participants satisfying industry standards. Further analysis of this design is warranted.

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国立情報学研究所で電子化したコンテンツを使用している。

With the rapid development of mobile device technology, smartphones are now not only the tool for young people but also for elderly people. However, the complicated steps of interacting with smartphones are stopping them from having a good user experience. One of the reasons is that application designers do not take consideration of the user group of elderly people. Our pilot survey shows that most elderly people lack the skills required to use a smartphone without obstacles, like typing. We also conducted an experiment with 8 participants that targeting on the usability of a daily used application, Contact List (CL), and based on a Chinese language system. We developed an android application that proposed a new method of showing the contact list according to the language usage of Chinese for this study. By asking participants to finish the same tasks on the traditional CL applications on their phones or on our application and observing their operations, we obtained useful feedback in terms of usability issues. Our experiment also tried to find out whether the method we proposed in the new application can lead to a better user experience for elderly people.

This research focused on the implementation of a reliable intelligent fall detection system so as to reduce accidental falls among the elderly people. A video-based detection system was used because it preserved privacy while monitoring the activities of the senior citizens. Another advantage of the video-based system is that the senior citizens are able to move freely without experiencing any hassles in wearing them as opposed to portable fall detection sensors so that they can have a more independent and happy life. A scientific research method was employed to improve the existing fall detection systems in terms of reliability and accuracy. This thesis consists of four stages where the first stage reviews the literature on the current fall detection systems, the second stage investigates the various algorithms of these existing fall detection systems, the third stage describes the proposed fall detection algorithm in detecting falls using two distinct approaches. The first approach deals with the use of specific features of the silhouette, an extracted binary map obtained from the subtraction of the foreground from the background, to determine the fall angle (FA), the bounding box (BB) ratio, the Hidden Markov Models (HMM) and the combination of FA, BB, and HMM. The second approach used is the neural network approach which is incorporated in the algorithm to identify a predetermined set of situations such as praying, sitting, standing, bending, kneeling, and lying down. The fourth stage involves the evalua- tion of the developed video-based fall detection system using different metrics which measure sensitivity (i.e. the capacity of the fall detection system to detect as well as declare a fall) and specificity (i.e. the capacity of the algorithm to detect only falls) of this algorithm. The video camera was properly positioned to avoid any occluding objects and also to cover a certain range of motion of the stunt participants performing the falls. The silhouette is extracted using an approximate median filtering approach and the threshold criteria value of 30 pixels was used. Morphological filtering methods that were dilation and erosion were used to remove any spurious noises from the extracted image prior to subsequent feature analysis. Then, this extracted silhouette was scaled and quantised using 8 bits/pixel and compared to the set of predetermined scenarios using a neural network of perceptrons. This neural network was trained based on various situations and the falls of the participants which represent inputs to the neural network algorithm during the neural learning process. In this research study, the built neural network consisted of 600 inputs, as well as 10 neurons in the hidden layer together with 7 distinct outputs which represent the set of predefined situations. Furthermore, an alarm generation algorithm was included in the fall detection algorithm such that there were three states that were STATE NULL (set at 0), STATE LYING (set at 1) and STATE ALL OTHERS (set at 2) and the initial alarm count was set to 90 frames (meaning 3 seconds of recorded consecutive images at 30 frames per second). Therefore, an alarm was generated only when the in-built counter surpassed this threshold of 90 frames to signal that a fall occurred. Following the evaluation stage, it was found that the combination of the first approach fall detection algorithm method (fall angle, bounding box, and hidden Markov) was 89% with specificity and 84.2% with sensitivity which is better than individual performance. Moreover, it was found that the second approach fall detection algorithm method (neural network performance) 94.3% of the scenarios were successfully classified whereby the specificity of the developed algorithm was determined to be 94.8% and the sensitivity was 93.8% which altogether show a promising overall performance of the fall detection video-based intelligent system. Moreover, the developed fall detection system were tested using two types of handicaps such as limping and stumbling stunt participants to observe how well this detection algorithm can detect falls as in the practical situations encountered or present in elderly people. In these cases it was found that about 90.2% of the falls were detected which showed still that the developed algorithm was quite robust and reliable subjected to these two physical handicaps motion behaviours.

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