![]() The multi-modal sensing framework for activity monitoring relies on parallel processing of videos and inertial data. This paper proposes an efficient and cost-effective multi-modal sensing framework for activity monitoring, it can automatically identify human activities based on multi-modal data, and provide help to patients with moderate disabilities. The development of activity recognition based on multi-modal data makes it possible to reduce human intervention in the process of monitoring. ![]() This research provides a unique opportunity to analyze the utility of camera modalities in detecting falls in a home setting while balancing performance, passiveness, and privacy. Our results showed that infra-red cameras provided the highest level of performance (AUC ROC=0.94), followed by thermal (AUC ROC=0.87), depth (AUC ROC=0.86) and RGB (AUC ROC=0.83). ![]() We formulated fall detection as an anomaly detection problem, in which a customized spatio-temporal convolutional autoencoder was trained only on ADLs so that a fall would increase the reconstruction error. These modalities offer benefits such as obfuscated facial features and improved performance in low-light conditions. To address these limitations, we introduce a novel multi-modality dataset (MUVIM) that contains four visual modalities: infra-red, depth, RGB and thermal cameras. The lack of these considerations makes it difficult to develop predictive models that can operate effectively in the real world. Many existing fall detection datasets lack important real-world considerations, such as varied lighting, continuous activities of daily living (ADLs), and camera placement. From a machine learning perspective, developing an effective fall detection system is challenging because of the rarity and variability of falls. Video cameras provide a passive alternative however, regular RGB cameras are impacted by changing lighting conditions and privacy concerns. Fall detection can be performed using wearable devices or ambient sensors these methods may struggle with user compliance issues or false alarms. Effective detection of falls can reduce the risk of complications and injuries. ![]() We conclude our paper by discussing several open research problems in the field and pointers for future research.įalls are one of the leading cause of injury-related deaths among the elderly worldwide. Then, we present a comprehensive literature review within those categories and identify the approach of treating a fall as an abnormal activity to be a plausible research direction. The taxonomy identifies different categories of classification methods for the study of fall detection based on the availability of their data during training the classifiers. The proposed taxonomy is independent of the type of sensors used and specific feature extraction/selection methods. In this paper, we present a taxonomy for the study of fall detection from the perspective of availability of fall data. Therefore, standard supervised machine learning methods may not be directly applied to handle this problem. Due to the rarity of occurrence of falls, there may be insufficient or no training data available for them. A fall is an abnormal activity that occurs rarely however, missing to identify falls can have serious health and safety implications on an individual.
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