Interview met Greet Baldewijns en Gert Mertes: Ingenieurs nemen intrek in rusthuis

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Detection of chewing motion in the elderly using a glasses mounted accelerometer in a real-life environment

This paper describes a method of detecting an elderly person's chewing motion using a glasses mounted accelerometer. A real-life dataset was collected from 13 elderly adults, aged 65 or older, during meal times in a care facility. A supervised classifier is used to automatically distinguish between epochs of chewing and non-chewing activity. Results are compared to a lab dataset of 5 young to middle-aged adults captured in previous work. K-Nearest Neighbor, Random Forest and Support Vector Machine classifiers are evaluated. All are able to achieve similar performance, with the Support Vector Machine performing the best with an F1-score of 0.73.status: publishe

Towards detection of chewing motion in the elderly using a glasses mounted accelerometer

In this work, we propose the use of a glasses mounted accelerometer to detect chewing motion in the elderly. Data from 13 elderly was collected during their daily meals. This data is used to evaluate a k-Nearest Neighbor classifier.status: publishe

Detection of chewing motion using a glasses mounted accelerometer towards monitoring of food intake events in the elderly

A novel way to detect food intake events using a wearable accelerometer is presented in this paper. The accelerometer is mounted on wearable glasses and used to capture the movements of the head. During meals, a person's chewing motion is clearly visible in the time domain of the captured accelerometer signal. Features are extracted from this signal and a forward feature selection algorithm is used to determine the optimal set of features. Support Vector Machine and Random Forest classifiers are then used to automatically classify between epochs of chewing and non-chewing. Data was collected from 5 volunteers. The Support Vector Machine approach with linear kernel performs best with a detection accuracy of 73.98% ± 3.99.status: publishe

Technologie op rust : het rusthuis van de toekomst

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Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms

Fall incidents are an important health hazard for older adults. Automatic fall detection systems can reduce the consequences of a fall incident by assuring that timely aid is given. The development of these systems is therefore getting a lot of research attention. Real-life data which can help evaluate the results of this research is however sparse. Moreover, research groups that have this type of data are not at liberty to share it. Most research groups thus use simulated datasets. These simulation datasets, however, often do not incorporate the challenges the fall detection system will face when implemented in real-life. In this Letter, a more realistic simulation dataset is presented to fill this gap between real-life data and currently available datasets. It was recorded while re-enacting real-life falls recorded during previous studies. It incorporates the challenges faced by fall detection algorithms in real life. A fall detection algorithm from Debard et al. was evaluated on this dataset. This evaluation showed that the dataset possesses extra challenges compared with other publicly available datasets. In this Letter, the dataset is discussed as well as the results of this preliminary evaluation of the fall detection algorithm. The dataset can be downloaded from www.kuleuven.be/advise/datasets.status: publishe

Ingenieurs in het rusthuis

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Improving the accuracy of existing camera based fall detection algorithms through late fusion

Fall incidents remain an important health hazard for older adults. Fall detection systems can reduce the consequences of a fall incident by insuring that timely aid is given. Currently fall detection algorithms however suffer a reduction in accuracy when introduced in real-life situations. In this paper a late fusion technique is proposed that will improve the accuracy of existing fall detection systems. It combines the confidence levels of different single camera fall detection systems. Four different aggregation methods are compared to each other based on the Area Under the Curve (AUC) of precision-recall curves. Calculating the median of the confidence levels of five cameras an increase of 218% in the AUC of the precision-recall-curves is achieved compared to the AUC of the single camera fall detector. These results show that significant improvements can be made to the accuracy of single camera fall detectors in a relatively easy way.status: publishe

Quantifying Eating Behavior With a Smart Plate in Patients With Arm Impairment After Stroke

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Measuring and localizing individual bites using a sensor augmented plate during unrestricted eating for the aging population.

Food intake monitoring can play an important role in the prevention of malnutrition in the aging population, but traditional tools may not be adequate for use in this target group. Traditional tools typically involve the use of questionnaires or food diaries that require manual data entry. Due to their time consuming nature, they are often incomplete, contain mistakes or not used at all. An alternative to self-reporting tools, in the form of a plate system that automatically measures the consumed food during the meal, is presented in this work. Furthermore, the system can estimate the location where each bite was taken on the plate. The system is compatible with an off-the-shelf plate that is mounted on top of a base station. Weight sensors need not be integrated in the plate itself. Localization of the bites is done by looking at the movement of the center of mass during eating. When used with a compartmentalized plate, the amount of consumed food per compartment can be estimated. With prior knowledge of the type of food in each compartment, this can give an indication of the amount of calories and nutritional intake. We present a bite detection algorithm using a random forest decision tree classifier. Data from 24 aging adults (ages 52-95) eating a single meal with chopsticks was used to train and evaluate the model. Out of a total of 836 true annotated bites, the algorithm detected 602 with a precision and recall of 0.78 and 0.76, respectively. By summing the weights of detected bites from each compartment, the algorithm was able to estimate the amount of food taken per compartment with an average error of (8 ±8)% of the portion size.status: publishe