• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of Hospice and Palliative Care
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• v.6 no.1
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• pp.22-33
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• 2003

Purpose : The purpose of this study was to evaluate the present status of patients' satisfaction and the reasons for any satisfaction or dissatisfaction in cancer pain management Methods : A cross-sectional survey was used to obtain the feedback about pain management. The results of the survey were collected from 59 in- or out-patient who had cancer treatment at two of the teaching hospitals in Seoul from July, 2002 to November, 2002. The data was obtained by a structured questionnaire based on the American Cancer Society Patient Outcome Questionnaire(APS-POQ) and other previous research. The clinical information for all patients were compiled by reviewing their medical records. Resuts : 1) The subjects' mean score of the worst pain was 6.77, the average pain score was 3.80, and the pain score after management was 2.93 for the past 24 hours. The mean score of total pain interference was $25.03{\pm}12.82$. Many of the subjects had false beliefs about pain such as 'the experience of pain is a sign that the illness has gotten worse', 'pain medicine should be 'saved' in case the pain gets worse' and 'people get addicted to pain medicine easily'. 2) 66.1% of the subjects were properly medicated with analgesics. 33.9% of the subjects reported use of various methods in controlling pain other than the prescribed medication. Only 33.9% of the subjects had a chance to be educated about pain management by doctors or nurses. 3) The mean score of patients' satisfaction with pain management was $4.19{\pm}1.14$. 72.9% of the subjects answered 'satisfied' with pain management. The reasons for dissatisfaction were 'the pain was not relieved even after the pain management', 'I was not quickly and promptly treated when I complained of pain', 'doctors and nurses didn't pay much attention to my complaints of pain.', and 'there was no appropriate information given on the methods of administration, effect duration and side effects of pain medicine.' The reasons for satisfaction were: 'the pain was relieved after the pain management.', 'doctors and nurses quickly and promptly controlled my pain.', 'doctors and nurses paid enough attention to my complaints of pain.' and 'trust in my physician'. 4) In pain severity or pain interference, no significant difference was found between the satisfied group and dissatisfied group. On the belief 'good patients avoid talking about pain', a significant difference was found between the satisfied group and dissatisfied group. Conclusions : The patients' satisfaction with cancer pain management has increased over the years but still about 30% of patients reported to be 'not satisfied' for various reasons. The results of this study suggest that patients' education should be done to improve satisfaction in the pain management program.