Browsing by Author "Alam, Mohammad Arif Ul"
Now showing 1 - 7 of 7
Results Per Page
Sort Options
Item AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment(2020-03-17) Alam, Mohammad Arif Ul; Roy, Nirmalya; Holmes, Sarah; Gangopadhyay, Aryya; Galik, ElizabethCognitive impairment has become epidemic in older adult population. The recent advent of tiny wearable and ambient devices, a.k.a Internet of Things (IoT) provides ample platforms for continuous functional and cognitive health assessment of older adults. In this paper, we design, implement and evaluate AutoCogniSys, a context-aware automated cognitive health assessment system, combining the sensing powers of wearable physiological (Electrodermal Activity, Photoplethysmography) and physical (Accelerometer, Object) sensors in conjunction with ambient sensors. We design appropriate signal processing and machine learning techniques, and develop an automatic cognitive health assessment system in a natural older adults living environment. We validate our approaches using two datasets: (i) a naturalistic sensor data streams related to Activities of Daily Living and mental arousal of 22 older adults recruited in a retirement community center, individually living in their own apartments using a customized inexpensive IoT system (IRB #HP-00064387) and (ii) a publicly available dataset for emotion detection. The performance of AutoCogniSys attests max. 93\% of accuracy in assessing cognitive health of older adults.Item Automated Functional and Behavioral Health Assessment of Older Adults with Dementia(IEEE, 2016-08-18) Alam, Mohammad Arif Ul; Roy, Nirmalya; Holmes, Sarah; Gangopadhyay, Aryya; Galik, ElizabethDementia is a clinical syndrome of cognitive deficits that involves both memory and functional impairments. While disruptions in cognition is a striking feature of dementia, it is also closely coupled with changes in functional and behavioral health of older adults. In this paper, we investigate the challenges of improving the automatic assessment of dementia, by better exploiting the emerging physiological sensors in conjunction with ambient sensors in a real field environment with IRB approval. We hypothesize that the cognitive health of older individuals can be estimated by tracking their daily activities and mental arousal states. We employ signal processing on wearable sensor data streams (e.g., Electrodermal Activity (EDA), Photoplethysmogram (PPG), accelerometer (ACC)) and machine learning algorithms to assess cognitive impairments and its correlation with functional health decline. To validate our approach, we quantify the score of machine learning, survey and observation based Activities of Daily Living (ADLs) and signal processing based mental arousal state, respectively for functional and behavioral health measures among 17 older adults living in a continuing care retirement community in Baltimore. We compare clinically observed scores with technology guided automated scores using both machine learning and statistical techniques.Item CACE: Exploiting Behavioral Interactions for Improved Activity Recognition in Multi-Inhabitant Smart Homes(IEEE, 2016-08-11) Alam, Mohammad Arif Ul; Roy, Nirmalya; Misra, Archan; Taylor, JosephWe 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 Bayesian Network (HDBN), which both (a) captures the hierarchical inference of complex (macro-activity) contexts from lower-layer microactivity context (postural and improved oral gestural context), and (b) embeds the various types of behavioral correlations and constraints (at both micro-and macro-activity contexts) across the individuals. While this model is rich in terms of accuracy, it is computationally prohibitive, due to the explosive increase in the number of jointly-defined states. To tackle this challenge, we employ data mining to learn behaviorally-driven context correlations in the form of association rules, we then use such rules to prune the state space dramatically. To evaluate our framework, we build a customized smart home system and collected naturalistic multi-inhabitant smart home activities data. The system performance is illustrated with results from real-time system deployment experiences in a smart home environment reveals a radical (max 16 fold) reduction in the computational overhead compared to traditional hybrid classification approaches, as well as an improved activity recognition accuracy of max 95%.Item Editorial for Special Issue on AI for Healthcare(now publishers, 2023-12-18) Purushotham, Sanjay; Woo, Jonghye; Zhang, Jing; Alam, Mohammad Arif Ul; Cheng, Jun; Gani, Md OsmanEditorial for the Special Issue on AI for HealthcareItem Mobeacon: An iBeacon-Assisted SmartphoneBased Real Time Activity Recognition Framework(EAI, 2015-08-11) Alam, Mohammad Arif Ul; Pathak, Nilavra; Roy, NirmalyaHuman 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 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. To navigate the above challenges, we build Mobeacon: a mobile phone and iBeacon sensor-based smart home activity recognition system. We investigated the viability of extending Bagging Ensemble Learning (BEL) and Packaged Naive Bayes (PNB) classification algorithms for high-level activity recognition on smartphone. We incorporated the semantic knowledge of the testing environment and used that with the built-in adaptive learning models on smartphone to ease the ground truth data annotation. We demonstrated that Mobeacon outperforms existing lightweight activity recognition techniques in terms of accuracy (max. 94%) in a low resource setting and proves itself substantially e fficient to reside on smartphones for recognizing ADLs in real time.Item Smart-Energy Group Anomaly Based Behavioral Abnormality Detection(IEEE, 2016-12-15) Alam, Mohammad Arif Ul; Roy, Nirmalya; Petruska, Michelle; Zemp, AndreaMonitoring behavioral abnormality of individuals living independently in their own homes is a key issue for building sustainable healthcare models in smart environments. While most of the efforts have been directed towards building ambient and wearable sensors-assisted activity recognition based behavioral analysis models for remote health monitoring, energy analytics assisted behavioral abnormality prediction have rarely been investigated. In this paper, we propose a data analytic approach that helps detect energy usage anomalies corresponding to the behavioral abnormality of the residents. Our approach relies on detecting everyday appliances usage from smart meter and smart plug data traces in regular activity days and then learning the unique time segment group of each appliance's energy consumption. We focus on detecting behavioral anomalies over a set of energy source data points rather than pinpointing individual odd points. We employ hierarchical probabilistic model-based group anomaly detection [7] to interpret the anomalous behavior and therefore, detect potential tendency towards behavioral abnormality. We apply daily activity logs to evaluate our approach using two realworld energy datasets pertaining to staged functional behaviors, and show that it is possible to detect max. 97% of anomalous days with max. 87% of meaningful micro-behavioral abnormal events generating 1.1% of false alarms. However, we show that our detected abnormality can be meaningfully represented to different stakeholders such as caregivers and family members to understand the nature and severity of abnormal human behavior for sustaining better healthcare.Item Unseen Activity Recognitions: A Hierarchical Active Transfer Learning Approach(IEEE, 2017-07-17) Alam, Mohammad Arif Ul; Roy, NirmalyaHuman activity recognition (AR) is an essential element for user-centric and context-aware applications. While previous studies showed promising results using various machine learning algorithms, most of them can only recognize the activities that were previously seen in the training data. We investigate the challenges of improving the recognition of unseen daily activities in smart home environment, by better exploiting the hierarchical taxonomy of complex daily activities. We first (a) design a hierarchical representation of complex activity taxonomy in terms of human-readable semantic attributes, and (b) develop a hierarchy of classifiers which incorporates a cluster tree built on the domain knowledge from training samples. Though this model is rich in recognizing complex activities that are previously seen in training data, it is not well versed to recognize unseen complex activities without new training samples. To tackle this challenge, we extend Hierarchical Active Transfer Learning (HATL) approach that exploits semantic attribute cluster structure of complex activities shared between seen (source) and unseen (target) activity domains. Our approach employs transfer and active learning to help label target domain unlabeled data by spawning the most effective queries. We evaluated our approach with two real-time smart home systems (IRB #HP-00064387) which corroborates radical improvements in recognizing unseen complex activities.