Based on the smart home and facial expression recognition, this paper presents a cognitive emotional model for eldercare robot. By combining with Gabor filter, Local Binary Pattern algorithm(LBP) and k-Nearest Neighbo...Based on the smart home and facial expression recognition, this paper presents a cognitive emotional model for eldercare robot. By combining with Gabor filter, Local Binary Pattern algorithm(LBP) and k-Nearest Neighbor algorithm(KNN) are facial emotional features extracted and recognized. Meanwhile, facial emotional features put influence on robot's emotion state, which is described in AVS emotion space. Then the optimization of smart home environment on the cognitive emotional model is specially analyzed using simulated annealing algorithm(SA). Finally, transition probability from any emotional state to a state of basic emotions is obtained based on the cognitive reappraisal strategy and Euclidean distance. The simulation and experiment have tested and verified the effective in reducing negative emotional state.展开更多
Nurses＇ unintentional medication errors during treatment are relatively frequent and yet inevitable. Errors provoke emotions which influence the nurses＇ professional careers. Little is known about the relationship b...Nurses＇ unintentional medication errors during treatment are relatively frequent and yet inevitable. Errors provoke emotions which influence the nurses＇ professional careers. Little is known about the relationship between nurses＇ supervisors constructive listening （CL） and the emotional reactions of nurses who committed an error and its relation to patients＇ safety. Our purpose was to explore the relationship between nurses＇ perceptions regarding their supervisors＇ CL and their emotional experiences after committing an error related to patient care. Dependent variables included of guilt, empathy towards the patient, general and professional self-assessment, shame, and Negative and Positive Affect （NA/PA）. In this descriptive study, we used a snowball sampling method. Participants were asked to sign an informed-consent form and complete the questionnaire before or after work. No compensation （material or otherwise） was offered to participants. The study was approved by the ethics committee of the academic institution involved. A total of 162 nurses participated： 103 （63.6%） held a registered and 40 （25%） held a managerial role. Seniority had high variability, ranging from 3 months to 45 years （M=1 3.54, SD=0.78）. The majority of errors reported （67.7%） concerned the administration of medications. We used Structural Equation Modeling to measure relationships between the main variables （X2（9）=14.52, p=.105, CFI=.911, RMSEA=.062 （90% CI=.00-. 11）. The main findings were： a high rating of perceived supervisor＇s CL led to high state-guilt （β=. 15, p=.04）. Next, higher state-guilt led to high PA （β=.18, p=.02） and to high NA （β=.45, p〈.001）. High PA led to reporting the error （β=.17, p=.03）, whereas high NA led to a high degree of empathy towards the patient （β=.17, p=.03）. Our findings show the importance of CL, which led to reporting error and to empathy towards patients, mediated by increased state-guilt and by increased positive and negative effect. Supervisor nurses should use CL to create an atmosphere of trust which fosters the reporting of errors and improves patients＇ safety.展开更多
基金supported by National Natural Science Foundation of China (Normal Project No. 61170115), (Key Project No.61432004)National Key Technologies R&D Program of China (No.2014BAF08B04)the Foundation of Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services
文摘Based on the smart home and facial expression recognition, this paper presents a cognitive emotional model for eldercare robot. By combining with Gabor filter, Local Binary Pattern algorithm(LBP) and k-Nearest Neighbor algorithm(KNN) are facial emotional features extracted and recognized. Meanwhile, facial emotional features put influence on robot's emotion state, which is described in AVS emotion space. Then the optimization of smart home environment on the cognitive emotional model is specially analyzed using simulated annealing algorithm(SA). Finally, transition probability from any emotional state to a state of basic emotions is obtained based on the cognitive reappraisal strategy and Euclidean distance. The simulation and experiment have tested and verified the effective in reducing negative emotional state.
文摘Nurses＇ unintentional medication errors during treatment are relatively frequent and yet inevitable. Errors provoke emotions which influence the nurses＇ professional careers. Little is known about the relationship between nurses＇ supervisors constructive listening （CL） and the emotional reactions of nurses who committed an error and its relation to patients＇ safety. Our purpose was to explore the relationship between nurses＇ perceptions regarding their supervisors＇ CL and their emotional experiences after committing an error related to patient care. Dependent variables included of guilt, empathy towards the patient, general and professional self-assessment, shame, and Negative and Positive Affect （NA/PA）. In this descriptive study, we used a snowball sampling method. Participants were asked to sign an informed-consent form and complete the questionnaire before or after work. No compensation （material or otherwise） was offered to participants. The study was approved by the ethics committee of the academic institution involved. A total of 162 nurses participated： 103 （63.6%） held a registered and 40 （25%） held a managerial role. Seniority had high variability, ranging from 3 months to 45 years （M=1 3.54, SD=0.78）. The majority of errors reported （67.7%） concerned the administration of medications. We used Structural Equation Modeling to measure relationships between the main variables （X2（9）=14.52, p=.105, CFI=.911, RMSEA=.062 （90% CI=.00-. 11）. The main findings were： a high rating of perceived supervisor＇s CL led to high state-guilt （β=. 15, p=.04）. Next, higher state-guilt led to high PA （β=.18, p=.02） and to high NA （β=.45, p〈.001）. High PA led to reporting the error （β=.17, p=.03）, whereas high NA led to a high degree of empathy towards the patient （β=.17, p=.03）. Our findings show the importance of CL, which led to reporting error and to empathy towards patients, mediated by increased state-guilt and by increased positive and negative effect. Supervisor nurses should use CL to create an atmosphere of trust which fosters the reporting of errors and improves patients＇ safety.
