Exploring Incomplete Rating Designs With Mokken Scale Analysis.

Recent research has explored the use of models adapted from Mokken scale analysis as a nonparametric approach to evaluating rating quality in educational performance assessments. A potential limiting factor to the widespread use of these techniques is the requirement for complete data, as practical constraints in operational assessment systems often limit the use of complete rating designs. In order to address this challenge, this study explores the use of missing data imputation techniques and their impact on Mokken-based rating quality indicators related to rater monotonicity, rater scalability, and invariant rater ordering.

Understanding smoking behavior using wearable sensors: relative importance of various sensor modalities.

The Personal Automatic Cigarette Tracker (PACT) system, which consists of abdominal (AB) and thoracic (TC) breathing sensors, and a RF hand-to-mouth proximity sensor (PS), has proven to be useful in the detection and characterization of cigarette smoke inhalations. In this research, we further analyze the impact of subjects' anthropometric characteristics on the quality of sensor signals and evaluate the contribution of each sensor modality to the accuracy of the classifier for smoke inhalations detection. Results indicated that subjects with medium BMI, high BMI, and in a standing position were, respectively, 1.

Detection of cigarette smoke inhalations from respiratory signals using reduced feature set.

A combination of wearable Respiratory Inductive Plethysmograph and a hand-to-mouth Proximity Sensor (PS) can be used to monitor smoking habits and smoke exposure in cigarette smokers. In our previous work, detection of smoke inhalations was achieved by using a Support Vector Machine (SVM) classifier applied to raw sensor signals with 1503-element feature vectors. This study uses empirically-defined 27 features computed from the sensor signals to reduce the length of vectors.

Detection of Hand-to-Mouth Gestures Using a RF Operated Proximity Sensor for Monitoring Cigarette Smoking.

Common methods for monitoring of cigarette smoking, such as portable puff-topography instruments or self-report questionnaires, tend to be biased due to conscious or unconscious underreporting. Additionally, these methods may change the natural smoking behavior of individuals. Our long term objective is the development of a wearable non-invasive monitoring system (Personal Automatic Cigarette Tracker - PACT) to reliably monitor cigarette smoking behavior under free living conditions.

Monitoring of cigarette smoking using wearable sensors and support vector machines.

Cigarette smoking is a serious risk factor for cancer, cardiovascular, and pulmonary diseases. Current methods of monitoring of cigarette smoking habits rely on various forms of self-report that are prone to errors and under reporting. This paper presents a first step in the development of a methodology for accurate and objective assessment of smoking using noninvasive wearable sensors (Personal Automatic Cigarette Tracker-PACT) by demonstrating feasibility of automatic recognition of smoke inhalations from signals arising from continuous monitoring of breathing and hand-to-mouth gestures by support vector machine classifiers.