Papers by Mohammad Anwar Ul Alam
arXiv (Cornell University), Sep 25, 2020
Staying hydrated and drinking fluids is extremely crucial to stay healthy and maintaining even ba... more 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, 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 realtime 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.
MobiQuitous 2020 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2020
Cognitive impairment has become epidemic in older adult population. The recent advent of tiny wea... more 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 AutoCog-niSys, 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.
arXiv (Cornell University), Jun 22, 2021
Person re-identification is a critical privacy attack in publicly shared healthcare data as per H... more Person re-identification is a critical privacy attack in publicly shared healthcare data as per Health Insurance Portability and Accountability Act (HIPAA) privacy rule. In this paper, we investigate the possibility of a new type of privacy attack, Person Re-identification Attack (PRI-attack) on publicly shared privacy insensitive wearable data. We investigate user's specific biometric signature in terms of two contextual biometric traits, physiological (photoplethysmography and electrodermal activity) and physical (accelerometer) contexts. In this regard, we develop a Multi-Modal Siamese Convolutional Neural Network (mmSNN) model. The framework learns the spatial and temporal information individually and combines them together in a modified weighted cost with an objective of predicting a person's identity. We evaluated our proposed model using real-time collected data from 3 collected datasets and one publicly available dataset. Our proposed framework shows that PPG-based breathing rate and heart rate in conjunction with hand gesture contexts can be utilized by attackers to re-identify user's identity (max. 71±3) from HIPAA compliant wearable data. Given publicly placed camera can estimate heart rate and breathing rate along with hand gestures remotely, person re-identification using them imposes a significant threat to future HIPAA compliant server which requires a better encryption method to store wearable healthcare data. CCS CONCEPTS • Security and privacy → Human and societal aspects of security and privacy; • Computing methodologies → Machine learning.
Effect of surfactant and sonication time on improving the bio-accessibility of lycopene synthesized in poly-lactic co-glycolic acid nanoparticles
2022 Houston, Texas July 17-20, 2022
Comparative Study of Lycopene Encapsulation Efficiency in Polycapprolactone Vs Poly Lactic Co-glycolic Acid
2019 Boston, Massachusetts July 7- July 10, 2019, 2019
Abstract. Lycopene contributes to the red-colored pigmentation of fruits and vegetables, and it i... more Abstract. Lycopene contributes to the red-colored pigmentation of fruits and vegetables, and it is a fat-soluble carotenoid with antioxidant properties. Epidemiological studies have shown the significant health benefits associated to the consumption of lycopene rich foods, because of the anti-cancer properties. Degradative losses of lycopene during processing is a grave concern, hence encapsulation provides a remedy. The objective of this study is to evaluate the encapsulation efficiency of two biodegradable polymers (PLGA and PCL) as for controlled release of lycopene in the gastrointestinal (GI) system. The nanoparticles (NP) were synthesized by emulsion evaporation method and physicochemical properties was determined using a Dynamic Light Scattering spectroscopy. The results show the hydrodynamic diameter of the lycopene NP synthesized in PCL (200 mg) and 3500 mg surfactant and sonicated for 15 min was 79.23±0.85 nm (Lowest). PLGA (500 mg) and 500 mg surfactant with 15 min sonication was observed to have the lowest NP diameter (108.2±2.66 nm) among the others. Significant difference result found in PDI value (0.12±0.07) when PCL of 200mg dissolve in 3500 mg of surfactant. On the other hands the zeta potential values were much smaller in case of PCL NP ranged between -1.3±0.046 and -4.21±0.08 mV compared to the PLGA NP -72.36±2.17 to 107.66±3.15 mV in all experiments. Thus, NP synthesized with PCL and surfactant provide a smaller sized nano-solution than PLGA and surfactant. As the degradation rate for PCL is lower than PLGA so PCL can be considered as a potential biodegradable polymer than PLGA to encapsulate lycopene. .
Effect of Conventional Heat and Microwave Pasteurization on Rheological Behavior of Lycopene Nanoemulsion
2021 ASABE Annual International Virtual Meeting, July 12-16, 2021, 2021
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Papers by Mohammad Anwar Ul Alam