Automatic Fall Monitoring: A Review

Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address

Automatic Fall Monitoring: A Review

Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address

Machine learning techniques for supporting dog grooming services

In recent years, there has been a remarkable surge in the popularity of dog grooming, which has resulted in a growing interest in leveraging cutting-edge technologies to streamline the process and enhance the overall experience. Specifically, computer vision and machine learning techniques have been garnering increasing attention as a means to assist dog groomers in classifying dog breeds. This paper explores the use of machine learning techniques based on Convolutional Neural Networks (CNNs) for classifying dog breeds and estimating the time required for bathing and grooming each dog. The study involves collecting a large dataset of images and corresponding grooming information for a diverse set of dog breeds. The effectiveness of the proposed method is evaluated using a range of performance metrics, including accuracy, precision, recall, and F1 score. Our study suggests that proposed CNNs can be valuable in helping dog owners and groomers identify the correct breed of a dog and estimate the grooming time before receiving the service. The accuracy of classification obtained by the proposed method achieves a 19% increase compared with other recently developed techniques. Finally, this work contributes to the development of a user-friendly application that allows customers to book dog grooming services, providing predictions for dog breeds and estimated grooming time

Analysis of Optimal Sensor Positions for Activity Classification and Application on a Different Data Collection Scenario

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted, and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and a dataset collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values greater than 96%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups