Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data / 대한의료정보학회지

OBJECTIVES: Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. METHODS: We first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. RESULTS: To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. CONCLUSIONS: In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios.

Development and Application of Direct Data Capture for Monitoring Medication Compliance in Clinical Trials / 대한의료정보학회지

OBJECTIVES: The monitoring of medication compliance in clinical trials is important but labor intensive. To check medication compliance in clinical trials, a system was developed, and its technical feasibility evaluated. METHODS: The system consisted of three parts: a management part (clinical trial center database and a developed program), clinical trial investigator part (monitoring), and clinical trial participant part (personal digital assistant [PDA] with a barcode scanner). The system was tested with 20 participants for 2 weeks, and compliance was evaluated. RESULTS: This study developed a medication compliance monitoring system that used a PDA with a barcode scanner, which sent reminder/warning messages, logged medication barcode data, and provided compliance information to investigators. Registered participants received short message service (SMS) reminder/warning messages on their PDA and sent barcode data at the dosing time. The age range of the participants was 29 to 73 years. Five participants were <50 years old and 8 were ≥65 years old. The total mean compliance rate was 82.3%. The mean compliance rate was 83.1% in participants <65 years old and 81.1% in those ≥65 years old. CONCLUSIONS: The system was feasible, usable, and effective, even with elderly participants, for monitoring medication compliance in clinical trials using a PDA with a barcode scanner, and may improve the quality of clinical trials.