arXiv subjects

Mohammad Arif Ul Alam

Publications and source records attributed to Mohammad Arif Ul Alam.

23 records · Page 2Linked to original sources

arXiv2021

Activity-Aware Deep Cognitive Fatigue Assessment using Wearables

Cognitive fatigue has been a common problem among workers which has become an increasing global problem since the emergence of COVID-19 as a global pandemic. While existing multi-modal wearable sensors-aided automatic cognitive fatigue monitoring tools have focused on physical and physiological sensors (ECG, PPG, Actigraphy) analytic on specific group of people (say gamers, athletes, construction workers), activity-awareness is utmost importance due to its different responses on physiology in different person. In this paper, we propose a novel framework, Activity-Aware Recurrent Neural Network (\emph{AcRoNN}), that can generalize individual activity recognition and improve cognitive fatigue estimation significantly. We evaluate and compare our proposed method with state-of-art methods using one real-time collected dataset from 5 individuals and another publicly available dataset from 27 individuals achieving max. 19% improvement.

eess.SP↗

arXiv2020

Monitoring My Dehydration: A Non-Invasive Dehydration Alert System Using Electrodermal Activity

Staying hydrated and drinking fluids is extremely crucial to stay healthy and maintaining even basic bodily functions. Studies have shown that dehydration leads to loss of productivity, cognitive impairment and mood in both men and women. However, there are no such an existing tool that can monitor dehydration continuously and provide alert to users before it affects on their health. In this paper, we propose to utilize wearable Electrodermal Activity (EDA) sensors in conjunction with signal processing and machine learning techniques to develop first time ever a dehydration self-monitoring tool, \emph{Monitoring My Dehydration} (MMD), that can instantly detect the hydration level of human skin. Moreover, we develop an Android application over Bluetooth to connect with wearable EDA sensor integrated wristband to track hydration levels of the users real-time and instantly alert to the users when the hydration level goes beyond the danger level. To validate our developed tool's performance, we recruit 5 users, carefully designed the water intake routines to annotate the dehydration ground truth and trained state-of-art machine learning models to predict instant hydration level i.e., well-hydrated, hydrated, dehydrated and very dehydrated. Our system provides an accuracy of 84.5% in estimating dehydration level with an sensitivity of 87.5% and a specificity of 90.3% which provides us confidence of moving forward with our method for larger longitudinal study.

cs.CY↗

arXiv2020

LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment

Veteran mental health is a significant national problem as large number of veterans are returning from the recent war in Iraq and continued military presence in Afghanistan. While significant existing works have investigated twitter posts-based Post Traumatic Stress Disorder (PTSD) assessment using blackbox machine learning techniques, these frameworks cannot be trusted by the clinicians due to the lack of clinical explainability. To obtain the trust of clinicians, we explore the big question, can twitter posts provide enough information to fill up clinical PTSD assessment surveys that have been traditionally trusted by clinicians? To answer the above question, we propose, LAXARY (Linguistic Analysis-based Exaplainable Inquiry) model, a novel Explainable Artificial Intelligent (XAI) model to detect and represent PTSD assessment of twitter users using a modified Linguistic Inquiry and Word Count (LIWC) analysis. First, we employ clinically validated survey tools for collecting clinical PTSD assessment data from real twitter users and develop a PTSD Linguistic Dictionary using the PTSD assessment survey results. Then, we use the PTSD Linguistic Dictionary along with machine learning model to fill up the survey tools towards detecting PTSD status and its intensity of corresponding twitter users. Our experimental evaluation on 210 clinically validated veteran twitter users provides promising accuracies of both PTSD classification and its intensity estimation. We also evaluate our developed PTSD Linguistic Dictionary's reliability and validity.

cs.CL↗

arXiv2020

AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment

Cognitive 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.

cs.HC↗

arXiv2019

Reflecting After Learning for Understanding

Today, image classification is a common way for systems to process visual content. Although neural network approaches to classification have seen great progress in reducing error rates, it is not clear what this means for a cognitive system that needs to make sense of the multiple and competing predictions from its own classifiers. As a step to address this, we present a novel framework that uses meta-reasoning and meta-operations to unify predictions into abstractions, properties, or relationships. Using the framework on images from ImageNet, we demonstrate systems that unify 41% to 46% of predictions in general and unify 67% to 75% of predictions when the systems can explain their conceptual differences. We also demonstrate a system in "the wild" by feeding live video images through it and show it unifying 51% of predictions in general and 69% of predictions when their differences can be explained conceptually by the system. In a survey given to 24 participants, we found that 87% of the unified predictions describe their corresponding images.

cs.AI↗