IntelliChair: a non-intrusive sitting posture and sitting activity recognition system

Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.

Modification of aftertaste with a menthol mouthwash reduces food wanting, liking, and ad libitum intake of potato crisps

This research investigated the effect of modifying the aftertaste of potato crisps on (1) temporal sensory perception and (2) appetite using three mouthwash conditions (no mouthwash, a water mouthwash, and a menthol mouthwash). For the sensory study, 17 screened female subjects were trained on the Temporal Dominance of Sensations (TDS) methodology. Subjects undertook TDS to monitor all sensory attributes during the mastication of a 2 g crisp until swallowing (at 20s), then conducted the mouthwash, and then continued the TDS task to monitor aftertaste until 90s. For the appetite study, 36 subjects (18 male, 18 female) completed 100 mm Visual Analogue Scales (VAS) for desire, liking, hunger, and thirst, followed by an ad libitum eating task. For the VAS scales testing, subjects chewed and swallowed a 2 g crisp, and then immediately conducted the mouthwash before completing the VAS scales. For the ad libitum task, subjects were given 12 min to consume as many crisps as they desired on a plate (up to 50 g). Every three minutes they were required to conduct a mouthwash. TDS results showed that in comparison with no mouthwash, the water mouthwash significantly reduced aftertaste attributes such as savoury, salty, and fatty mouthcoating, and the menthol mouthwash significantly increased aftertaste attributes of cooling, minty, and tingly. The water mouthwash did not influence desire and liking of crisps, or hunger and thirst. The water mouthwash did not influence ad libitum intake of the crisps over a 12 min period. The menthol mouthwash significantly reduced desire and liking of the crisps, as well as hunger and thirst. Furthermore, the menthol mouthwash significantly reduced ad libitum crisp intake by 29% over the 12 min period.