Social skills training, performed by human trainers, is a well-established method for obtaining appropriate skills in social interaction. Previous work automated the process of social skills training by developing a dialogue system that teaches social communication skills through interaction with a computer avatar. Even though previous work that simulated social skills training only considered acoustic and linguistic information, human social skills trainers take into account visual and other non-verbal features. In this paper, we create and evaluate a social skills training system that closes this gap by considering the audiovisual features of the smiling ratio and the head pose (yaw and pitch). In addition, the previous system was only tested with graduate students; in this paper, we applied our system to children or young adults with autism spectrum disorders. For our experimental evaluation, we recruited 18 members from the general population and 10 people with autism spectrum disorders and gave them our proposed multimodal system to use. An experienced human social skills trainer rated the social skills of the users. We evaluated the system’s effectiveness by comparing pre- and post-training scores and identified significant improvement in their social skills using our proposed multimodal system. Computer-based social skills training is useful for people who experience social difficulties. Such a system can be used by teachers, therapists, and social skills trainers for rehabilitation and the supplemental use of human-based training anywhere and anytime.

This paper proposes a new approach to automatically detect dementia. Even though some works have detected dementia from speech and language attributes, most have applied detection using picture descriptions, narratives, and cognitive tasks. In this paper, we propose a new computer avatar with spoken dialog functionalities that produces spoken queries based on the mini-mental state examination, the Wechsler memory scale-revised, and other related neuropsychological questions. We recorded the interactive data of spoken dialogues from 29 participants (14 dementia and 15 healthy controls) and extracted various audiovisual features. We tried to predict dementia using audiovisual features and two machine learning algorithms (support vector machines and logistic regression). Here, we show that the support vector machines outperformed logistic regression, and by using the extracted features they classified the participants into two groups with 0.93 detection performance, as measured by the areas under the receiver operating characteristic curve. We also newly identified some contributing features, e.g., gap before speaking, the variations of fundamental frequency, voice quality, and the ratio of smiling. We concluded that our system has the potential to detect dementia through spoken dialog systems and that the system can assist health care workers. In addition, these findings could help medical personnel detect signs of dementia.

The aim of the present study was to determine the effect of sulfaphenazole (SP) on the pharmacokinetics of ampiroxicam (AM) which is metabolized by cytochrome P-450 (CYP) 2C9, since SP is a potent inhibitor of CYP 2C9, and so a dramatic pharmacokinetic drug interaction between both drugs is assumed after dosing. Single intravenous and oral administrations of AM (5 and 7.5 mg/kg piroxicam equivalent, respectively) and SP (80 and 120 mg/kg, respectively) to rats did not significantly alter the elimination kinetics of AM and piroxicam (PX) converted from AM. When SP was preloaded orally at 2 h before the dosing of AM, and when AM and SP were orally coadministered for 7 d, the elimination of PX from plasma was slightly retarded and the area under the plasma concentration-time curve (AUC) was increased 77 and 53%, respectively, but not significantly, compared with those after AM alone. On the other hand, a significantly decreased metabolic conversion of PX to 5'-hydroxyPX in plasma was observed by these treatments (p<0.05). In order to clarify the mechanism for the interaction, hepatic and intestinal metabolizing enzyme activities, CYP, uridine 5'-diphosphoglucuronyltransferase (UDPGT) and aryl esterase, were assayed after single and multiple oral administrations of AM or AM and SP. The enzyme activities were hardly inhibited by the treatment, indicating that the inhibition of CYP and hydrolytic enzymes by SP was approximately denied. These results suggest that SP does not significantly affect the pharmacokinetics of AM and PX in rats. However, the pharmacokinetic drug interaction between both drugs in man may not always be ignored.

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Swim-up separation following Percoll gradient centrifugation should offer adequate protection for HIV-1-discordant couples.

There are a large number of computer-based systems that aim to train and improve social skills. However, most of these do not resemble the training regimens used by human instructors. In this paper, we propose a computer-based training system that follows the procedure of social skills training, a well-established method to decrease human anxiety and discomfort in social interaction, and acquire social skills. In this paper, we attempt to automate the process of social skills training by developing a dialogue system named the "automated social skills trainer," which teaches social communication skills through human-agent interaction. The system includes a virtual avatar that recognizes user speech and language information and gives feedback to users. Its design is based on conventional social skills training performed by human participants, including defining target skills, modeling, role-play, feedback, reinforcement, and homework. We performed a series of three experiments investigating 1) the advantages of using computer-based training systems compared to human-human interaction by subjectively evaluating nervousness, ease of talking, and ability to talk well, 2) the relationship between speech language features and human social skills, and 3) the effect of computer-based training using our proposed system. Results of our first experiment show that interaction with an avatar decreases nervousness and increases the user's subjective impression of their ability to talk well compared to interaction with an unfamiliar person. The experimental evaluation measuring the relationship between social skill and speech and language features shows that these features have a relationship with social skills. Finally, experiments measuring the effect of performing social skills training with the proposed application show that participants significantly improve their skill, as assessed by separate evaluators, by using the system for 50 minutes. A user survey also shows that the users thought our system is useful and easy to use, and interaction with the avatar felt similar to human to human interaction.

Social skills training is a well-established method to decrease human anxiety and discomfort in social interaction, and acquire social skills. In this paper, we attempt to automate the process of social skills training by developing a dialogue system named "automated social skills trainer," which provides social skills training through human-computer interaction. The system includes a virtual avatar that recognizes user speech and language information and gives feedback to users to improve their social skills. Its design is based on conventional social skills training performed by human participants, including defining target skills, modeling, role-play, feedback, reinforcement, and homework. An experimental evaluation measuring the relationship between social skill and speech and language features shows that these features have a relationship with autistic traits. Additional experiments measuring the effect of performing social skills training with the proposed application show that most participants improve their skill by using the system for 50 minutes.

BackgroundOnline or Web-based measurement systems have been proposed as convenient methods for collecting physical activity data. We developed two Web-based physical activity systems—the 24-hour Physical Activity Record Web (24hPAR WEB) and 7 days Recall Web (7daysRecall WEB).ObjectiveTo examine the validity of two Web-based physical activity measurement systems using the doubly labeled water (DLW) method.MethodsWe assessed the validity of the 24hPAR WEB and 7daysRecall WEB in 20 individuals, aged 25 to 61 years. The order of email distribution and subsequent completion of the two Web-based measurements systems was randomized. Each measurement tool was used for a week. The participants’ activity energy expenditure (AEE) and total energy expenditure (TEE) were assessed over each week using the DLW method and compared with the respective energy expenditures estimated using the Web-based systems.ResultsThe mean AEE was 3.90 (SD 1.43) MJ estimated using the 24hPAR WEB and 3.67 (SD 1.48) MJ measured by the DLW method. The Pearson correlation for AEE between the two methods was r = .679 (P < .001). The Bland-Altman 95% limits of agreement ranged from –2.10 to 2.57 MJ between the two methods. The Pearson correlation for TEE between the two methods was r = .874 (P < .001). The mean AEE was 4.29 (SD 1.94) MJ using the 7daysRecall WEB and 3.80 (SD 1.36) MJ by the DLW method. The Pearson correlation for AEE between the two methods was r = .144 (P = .54). The Bland-Altman 95% limits of agreement ranged from –3.83 to 4.81 MJ between the two methods. The Pearson correlation for TEE between the two methods was r = .590 (P = .006). The average input times using terminal devices were 8 minutes and 10 seconds for the 24hPAR WEB and 6 minutes and 38 seconds for the 7daysRecall WEB.ConclusionsBoth Web-based systems were found to be effective methods for collecting physical activity data and are appropriate for use in epidemiological studies. Because the measurement accuracy of the 24hPAR WEB was moderate to high, it could be suitable for evaluating the effect of interventions on individuals as well as for examining physical activity behavior.