Yang-Yen Ou

A happiness-oriented home care system for elderly daily living

Currently the modern developing home-care systems highlight the functionalities on bio-signals measurement, security surveillance and health care, however most of them work independently. In this paper, a newly warming-care framework for elderly is proposed, not only to reach the aforementioned services, but also including following kindly services, that is, the remote monitoring, the web camera management, the emergency call for help, the behavior recognition and feedback, and the remote control entertainment services, to reach a comprehensive humanistic-caring system. The proposed framework is motivated by the individual alphabet on “HAPPINESS” which are redefined and interpreted as “Health”, “Ability”, “Protection”, “Personalization”, “Interaction”, “Nursing”, “Entertainment”, “Succor” and “Smile”. Three main services are spotlighted to achieve the goals described below. The Web-based Central Camera Management Service (WCCMS) is a real-time remote monitoring function that a caregiver can pay attention to care elderly anytime and anywhere through web services; the Multimodal Human-Machine Interaction Service (MHMIS) provides the audio-visual cognitive functions to interact with elderly, and the Web-based User Management Service (WUMS) gives user a smart HMI interface including bio-signal measurement, help button, remote control, and hospital appointment scheduling functionalities. To evaluate the proposed framework usability, MOS (Mean Opinion Score) is applied and average MOS 4.2 score is acquired that reveals the proposed system expectable.

SVM-based IADL score correlation and classification with EEG/ECG signals

This paper explores the correlation between the subjective IADL assessment and the objective EEG/ECG signals measurement. Thirty elderly participants are scored by IADL and classified into three groups, that is, the high score, the medium score and the low score groups, and each participants collected EEG/ECG signals is then attributed to the groups correspondingly. Six equations of extraction methods, including five for EEG and one for ECG, are applied to the EEG/ECG signals from each participant. Thereafter, the extracted features are trained by SVM and classified by one-against-all method in terms of group. The experiment is shown that 82% of accuracy can be reached by the proposed extracted methods and the proposed framework.

Spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition

This work presents a spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition. The semantic content is compressed and classified by factor categories from spoken dialog. The transcription of automatic speech recognition is then processed through Chinese Knowledge and Information Processing segmentation system. The proposed system also adopts the part-of-speech tags to effectively select and rank the keywords. Finally, the HAPPINESS/SUFFERING factor recognition is done by the proposed point-wise mutual information. Compared with the original method, the performance is improved by applying the significant scores of keywords. The experimental results show that the average precision rate for factor recognition in outside test can reach 73.5% which demonstrates the possibility and potential of the proposed system.

An interactive musical application with hand motion and gesture controlled interface

The paper proposes a system which uses an intuitive motion controlled interface to provide more effective method for music creation. The system uses motion sensing device to sense hand motions and gestures, which are used to perform various functions within the application program. The proposed interface improves the level of interactions, as well as the ease of control between the user and the system, and the new human computer interface increases the joy and pleasure of the program.

Cheerful jumping shot for happiness detection

Cheerful jumping is a kind of exaggerative behavior that implies human is surrounding in a short-term happiness. To capture the cheerful jumping manually in an instance by smartphone camera is still not well-functional. In this paper, we proposes a Kinect framework by using three key elements, including skeleton tracking, depth and color information in Kinect to explore the cheerful jumping shot, of which the framework fundamentally measures the sensed mapping displacement of jumping to-and-fro among the skeleton tracking system, depth and color information. Besides, a threshold λ is proposed to evaluate the users whose different jumping capabilities in different gender. The experimental result indicates the proposed work is able to capture more sharpen jumping result compared to the general smartphone camera.

A GPS Tracking Device Embedded in Prayer Beads for Early-Stage Dementia Detection

This paper proposes a prototyping of a prayer beads embedded with a GPS device, which is applied for tracking the trajectory of elderly to possibly detect the early stage of Dementia, of which the chronic diseases for elderly will lead to the deterioration in memory, thinking, behavior and ability, thus caused to the forgetfulness, losing track or losing familiar places gradually in their daily life. The prototyping mainly consists of two blocks, including, the power supply block and the computation & communication block, in which three modules, i.e. microcontroller, GSM/GPRS module and GPS module are functional and enabled by the power supply block. To timely transmit the GPS trajectory from the prototyping wore on elderly to the caregiver on their smart device or laptop anytime and anywhere, a cloud service system is proposed to coordinate the GPS information processed among the proposed GPS-based prayer beads, the cloud service system and the caregivers mobile device. Eventually, the simulation result is shown that the trajectory of elderly can be detected and recorded through the proposed framework at a convenience manner, however, some obstacles e.g. trees, building, viaduct etc. during the tracking will tentatively lose connection and influence the positioning accuracy, that will be overcome in the future work.

Instrumental activities of daily living (IADL) evaluation system based on EEG signal feature analysis

This work proposes an IADL evaluation system using LDA algorithm based on EEG signal, which explores the correlation between the subjective IADL assessment and the objective EEG signals measurement. Five features are extracted from the single channel EEG device including average amplitude, power ratio, spectral central, spectral edge frequency 25% and 50%. These features are represented as an indicator of participants IADL and are classified as IADL scales using LDA algorithm. For system evaluation, thirty elderly participants (70 ~ 96 years old) are classified into three groups by IADL score: high (disability-free, 16~24 points), medium (mild disability, 8 ~ 15 points) and low (severe disability, 0 ~ 7 points). These IADL groups distribute uniformly to conduct following IADL scenarios; 1. Ability to use telephone, 2. Ability to handle finances, and 3. Chat with people (that is not included in IADL scenario). The experiment result shows that the proposed EEG features and evaluation system can achieve 90% average accuracy rate verified by Leave-One-Out cross validation (LOOCV).