Smart Home Activity and Health Analytics

Multi-inhabitant Activity Recognition

Recognizing activities of multiple inhabitants poses fundamental challenges of defining, classifying, training, building, and scaling the underlying state space model in an effective way. Scaling the activity recognition techniques beyond a single inhabitant requires efficient design of novel algorithmic techniques to handle the exponential increase in the number of states present in a multi-inhabitant scenario. In this project, we have been exploiting the spatiotemporal constraints and correlations for recognizing intra- and inter-relationships across multiple inhabitants at the micro, meso and macro-level of activities to build an activity model which scale beyond an individual inhabitant and single smart home.

Real Time Activity Recognition

Automated activity recognition enables a wide variety of applications related to functional, behavioral, and cognitive health, disease diagnosis and treatment, personal health and wellness management to name a few. 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 has become a viable and urgent avenue. Although variants of online and offline lightweight activity recognition algorithms have been developed, realizing them on real time to recognize people’s activities is still a challenging research problem due to the computational complexity of building, training, learning and storing activity models in resource constrained smart and wearable devices in presence of an impeding scarcity of just-in-time annotated datasets. To navigate the above challenges, we have been exploring to build lightweight activity recognition algorithm and design their use cases in practice.

Sleep Monitoring for Community Scaling

Following healthy lifestyle is a key for active living. Regular exercise, controlled diet and sound sleep play an invisible role on the wellbeing and independent living of the people. Sleep being the most durative activities of daily living (ADL) has a major synergistic influence on people’s mental, physical, and cognitive health. Understanding the sleep behavior and its underpinning clausal relationships with physiological signals and contexts (such as eye or body movement etc.) responsible for a sound or disruptive sleep pattern help provide meaningful information for promoting healthy lifestyle and designing appropriate intervention strategy. In this project, we are working on detecting the microscopic states of the sleep which fundamentally constitute the components of a good or bad sleeping behavior and help shape the formative assessment of sleep quality. For a larger deployment of our proposed model across a community of individuals we have been investigating an active learning-based methodology for reducing the effort of ground truth data collection.

Behavioral Health Prediction

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 approaches can not only help identify the daily activities or social interaction of the older adults but also provide unique insights into their long-term health care, wellness management and ambulatory conditions. Gestural activities (GAs), in general, help identify fine-grained physiological symptoms and chronic psychological conditions which are not directly observable from traditional activities of daily living. In this project, we are developing GeSmart, an energy efficient wearable smart earring based gestural activity recognition model for detecting a combination of speech and non-speech events.



Project Sponsor:

This project is funded, in part, by the UMB-UMBC Research and Innovation Partnership Grant. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Sponsored Agency.


Mohammad Arif Ul Alam (Ph.D. student), H M Sajjad Hossain (Ph.D. student), Md Abdullah Al Hafiz Khan (Ph.D. student), Joseph Taylor (undergraduate student), Dr. Nirmalya Roy (MPSC director).

Collaborators: Dr. Elizabeth Galik (Prof. of Nursing at UMB), Dr. Aryya Gangopadhyay (Prof. of Information Systems at UMBC), Dr. Zhiyuan Chen (Prof. of Information Systems at UMBC), Dr. Gunes Koru (Prof. of Information Systems at UMBC), Dr. Nilanjan Banerjee (Prof. of CSEE at UMBC), Dr. Judah Ronch (Prof. at Erickson School of Aging), and Dr. Diane Cook (Prof. of EECS at WSU).


    1. H M Sajjad Hossain, Nirmalya Roy, and Hafiz Khan. “Sleep Well: A Sound Sleep Monitoring Framework for Community Scaling”, in Proceeding of the IEEE International Conference on Mobile Data Management (MDM), June 2015.
    2. Mohammad Arif Ul Alam and Nirmalya Roy, “Gesmart: A gestural activity recognition model for predicting behavioral health”, in Proceeding of the IEEE International Conference on Smart Computing (SmartComp), November 2014.
    3. Nirmalya Roy and Christine Julien, “Immersive physiotherapy: Challenges for smart living environments and inclusive communities”, in Proceeding of the 12th International Conference on Smart Homes and Health Telematics (ICOST), June 2014.
    4. Nirmalya Roy and Brooks Reed Kindle, “Monitoring patient recovery using wireless physiotherapy devices”, in Proceeding of the 12th International Conference on Smart Homes and Health Telematics (ICOST), June 2014.


Nirmalya Roy, H M Sajjad Hossain, and Mohammad Arif Ul Alam. “Scalabe Activity Recognition for Independent Living Applications”, UMBC Ref. No. 2015-004.