Mohammad Arif Ul Alam

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To promote independent living for elderly population activity recognition based approaches have been investigated deeply to infer the activities of daily living (ADLs) and instrumental activities of daily living (I-ADLs). Deriving and integrating the gestural activities (such as talking, coughing, and deglutition etc.) along with activity recognition(More)
Human activity recognition using multi-modal sensing technologies to automatically collect and classify daily activities has become an active field of research. Given the proliferation of smart and wearable devices and their greater acceptance in human lives, the need for developing real time lightweight activity recognition algorithms become a viable and(More)
As computing devices continue to become more heavily integrated into our lives, proper design of human-computer interfaces becomes a more important topic of discussion. Efficient and useful human-computer interfaces need to take into account the abilities of the humans who will be using such interfaces, and adapt to difficulties that different users may(More)
Privacy is the most often cited criticism of context awareness in pervasive environments. Context aware pervasive applications have the vulnerabilities of capturing extensive portions of users' activities. Whether such data capture is an actual threat or not, users' perceptions of such possibilities may discourage them from using many useful pervasive(More)
We propose CACE (Constraints And Correlations mining Engine) which investigates the challenges of improving the recognition of complex daily activities in multi-inhabitant smart homes, by better exploiting the spatiotemporal relationships across the activities of different individuals. We first propose and develop a loosely-coupled Hierarchical Dynamic(More)
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