Smart Home Healthcare Systems Research Articles

Smart Home Healthcare System Based on Middleware and Counter Neural Network

In recent years, with the upsurge of the smart city construction, smart communities and smart medical services have also witnessed rapid development. The development of smart home has achieved remarkable results. The new term of smart home medical treatment is also accompanied by intelligent home into people’s lives. Smart home health is to improve the smart home to bring new experiences of the public life, combined with the wisdom of the medical under another kind of human considerations. The emergence of smart medical treatment is accompanied by smart home and in an attempt to solve the current imbalance in the relationship between doctors and also patients in China, especially the common medical sanitation in hospitals with high sanction, and the heavy workload of hospitals and the contradiction between doctor-patient relationships. Under this general background, this paper proposes smart home healthcare system based on the middleware and counter neural network model. The simulation proves the efficiency.

A robust human activity recognition system using smartphone sensors and deep learning

In last few decades, human activity recognition grabbed considerable research attentions from a wide range of pattern recognition and human–computer interaction researchers due to its prominent applications such as smart home health care. For instance, activity recognition systems can be adopted in a smart home health care system to improve their rehabilitation processes of patients. There are various ways of using different sensors for human activity recognition in a smartly controlled environment. Among which, physical human activity recognition through wearable sensors provides valuable information about an individual’s degree of functional ability and lifestyle. In this paper, we present a smartphone inertial sensors-based approach for human activity recognition. Efficient features are first extracted from raw data. The features include mean, median, autoregressive coefficients, etc. The features are further processed by a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) to make them more robust. Finally, the features are trained with a Deep Belief Network (DBN) for successful activity recognition. The proposed approach was compared with traditional expression recognition approaches such as typical multiclass Support Vector Machine (SVM) and Artificial Neural Network (ANN) where it outperformed them.

Patients' Adoption of WSN-Based Smart Home Healthcare Systems: An Integrated Model of Facilitators and Barriers

Background: Patient-centered care emphasizes care coordination and communication through active involvement of patients, their families, physicians, and other professionals to improve decision making. Smart telecommunication technology and the Internet of Things, such as wireless-sensor-network-based smart home healthcare systems (WSN-SHHS) facilitate communication and collaboration among these different roles. Research problem: Despite the great potential of such systems to improve the quality and experience, and lower the cost of health care, the technology has not been widely adopted partly due to an inadequate understanding of user expectations, needs, and preferences. This study addresses facilitators and barriers with regard to WSN-SHHS adoption by identifying important sociotechnical, cognitive, affective, and contextual factors. Research questions: What are the main facilitators and barriers of patients' adoption of WSN-SHHS? How can we contextualize a generic technology adoption model for WSN-SHHS that takes into account unique characteristics of the domain? Literature review: We surveyed the literature in WSN-SHHS research and application, technology adoption theories, and the pleasure-arousal-dominance emotional state model. We discovered that WSN-SHHS research has focused on technology development but has given little attention to the issue of patients' adoption. Methodology: We used a mixed method design that combined an interview and survey over two studies. Participants were recruited from home healthcare agencies in the eastern US. In semistructured interviews, we collected data from 15 home healthcare patients and medical professionals, and analyzed the data using Kvale's approach. In our online- and paper-based surveys, we analyzed the data from 140 respondents using partial least square. Results and conclusions: We identified several new constructs in relation to WSN-SHHS adoption, including human detachment concerns, privacy concerns, life-quality expectancy and cost concerns. In addition, we confirmed the constructs from the general adoption model. Based on the findings of the qualitative study, the researchers created a research model. The quantitative study provided empirical support for the model, which has substantial predictive power accounting for more than half of the variance in WSN-SHHS adoption. In particular, our findings reveal that human detachment concerns rather than performance expectancy is the strongest predictor of patients' adoption of WSN-SHHS.

Human fall detection in surveillance video based on PCANet

Fall incidents have been reported as the second most common cause of death, especially for elderly people. Human fall detection is necessary in smart home healthcare systems. Recently various fall detection approaches have been proposed., among which computer vision based approaches offer a promising and effective way. In this paper, we proposed a new framework for fall detection based on automatic feature learning methods. First, the extracted frames, including human from video sequences of different views, form the training set. Then, a PCANet model is trained by using all samples to predict the label of every frame. Because a fall behavior is contained in many continuous frames, the reliable fall detection should not only analyze one frame but also a video sequence. Based on the prediction result of the trained PCANet model for each frame, an action model is further obtained by SVM with the predicted labels of frames in video sequences. Experiments show that the proposed method achieved reliable results compared with other commonly used methods based on the multiple cameras fall dataset, and a better result is further achieved in our dataset which contains more training samples.

노인의 스마트 홈 헬스케어 이용 경험

목적: 최근 고령화, 만성질환의 증가로 가정 내 소형 정보통신기기를 비치하고 네크워크로 연결하여 건강상태를 모니터링하며 지속적인 건강관리를 가능하게 하는 기술 및 서비스가 시도되고 있다. 본 연구는 서울시 영구임대아파트 단지에서 시행한 스마트 홈 헬스케어 기술을 제공받은 노인들을 대상으로 이용 경험을 파악하기 위해 시행되었다. 방법: 고혈압 또는 당뇨를 가진 총 7명의 노인(평균연령 75세)에게 2013년 7월, 2회에 걸친 포커스그룹 인터뷰를 통해 자료수집이 이루어졌으며, 질적 내용분석방법으로 분석하였다. 결과: 총 27개의 의미있는 자료가 추출되어 코드화 한 후 비교, 대조, 분류 과정을 통해 10개의 카테고리로 재구성하였고, 추상화를 거쳐 총 6개의 주제를 도출하였다. 도출된 주제는 '언제 어디서나 자가 측정으로 건강상태호전을 경험함', '가정에서 전문가 도움을 받는 흥미로운 체험', '잦은 시스템 및 통신장애로 사용하기에 답답함', '노인사용자에게 측정기기는 낯설고 불편함', '활성화를 위한 체계적인 전략보완이 요구됨', '고령 친화적 측정기기 및 프로그램의 업그레이드 필요'이다. 결론: 향후 스마트 홈 헬스케어를 활성화하기 위해서는 노인사용자의 특성을 고려한 고령 친화적인 측정기기 및 프로그램이 개발되고 최적의 전문인력 배치 및 관련단체의 적극적인 지원 속에서 추진되어야 할 것이다. Purpose: This study was conducted to explore the experience of smart-home healthcare among older adults who had participated in the smart-home technology development project. Methods: Data were collected by focus group interviews with 7 hypertension or diabetes participants(mean age 75 years). Qualitative contents analysis was used to analyze the verbal data. Results: The results were composed of 27 meanings units, 10 categories and 6 themes. The 6 themes are: 'improvement of health conditions through anytime anywhere self-monitoring', 'attractive experience to be counselling at home', 'frequent system and telecommunication problems', 'unfamiliar devices for elderly users', 'complementary strategies for activation', 'advancement of elderly-friendly devices and programs'. Conclusion: The smart-home healthcare system should be developed based on older adults' preferences. It is essential to make progress with professional resources and organizational supports in the future.