Smartphone Health Biomarkers: Positive Unlabeled Learning of In-the-Wild Contexts

Alajaji, Abdulaziz; Gerych, Walter; Buquicchio, Luke; Chandrasekaran, Kavin; Mansoor, Hamid; Agu, Emmanuel; Rundensteiner, Elke A.

doi:10.1109/mprv.2021.3051869

Cited by 6 publications

(7 citation statements)

References 27 publications

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“…Our Lab-to-field method tries to leverage a scripted dataset that contains high-quality, relatively cheaper to obtain, ground truth labels to improve HCR model performance on an in-the-wild dataset [ 9 ]. The ability of deep neural networks to generalize to real-world scenarios, where domain shift is expected, is a critical challenge in smartphone HCR developed for in-the-wild data [ 1 , 23 ]. Importance re-weighting [ 9 , 16 ] and Positive Unlabeled (PU) classifiers [ 1 ] are two methods that have been presented in the past to deal with covariate shifts.…”

Section: Related Workmentioning

confidence: 99%

“…There is a great potential for context-aware (CA) systems to impact many fields, such as healthcare, smart homes, and security [ 1 ]. An important part of CA systems is Human Context Recognition (HCR), the process of determining the user’s current state.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, to support our DA strategy, we used HCR datasets with coincident scripted and in-the-wild data with equivalent context labels collected in both studies [ 1 ]. These coincident datasets and identical context labels guarantee that there is a feature representation of contexts that is shared throughout scripted and unscripted datasets, which is a crucial need for the DA strategy.…”

Section: Introductionmentioning

confidence: 99%

“…There is a paucity of research on laboratory-to-field generalization approaches for HCR. Previously proposed lab-to-field methods include importance re-weighting [ 9 , 16 ] and Positive Unlabeled (PU) classifiers [ 1 ]. DA has been used in the past to solve the problem of variable on-body locations of wearable sensors [ 5 , 17 ] but not for HCR.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, in addition to using a domain alignment loss [ 18 , 19 ], Triple-DARE improves intra-class compactness and inter-class separability by utilizing a joint fusion triplet loss [ 12 , 13 ] intended for multi-labeled datasets. Moreover, unlike other existing methods for dealing with domain shifts [ 1 , 9 , 17 , 20 ], we do not utilize target labels in the target (in-the-wild) dataset, instead following the UDA problem setting outlined by Chang et al [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Domain Adaptation Methods for Lab-to-Field Human Context Recognition

Alajaji

Gerych

Buquicchio

et al. 2023

Sensors

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Human context recognition (HCR) using sensor data is a crucial task in Context-Aware (CA) applications in domains such as healthcare and security. Supervised machine learning HCR models are trained using smartphone HCR datasets that are scripted or gathered in-the-wild. Scripted datasets are most accurate because of their consistent visit patterns. Supervised machine learning HCR models perform well on scripted datasets but poorly on realistic data. In-the-wild datasets are more realistic, but cause HCR models to perform worse due to data imbalance, missing or incorrect labels, and a wide variety of phone placements and device types. Lab-to-field approaches learn a robust data representation from a scripted, high-fidelity dataset, which is then used for enhancing performance on a noisy, in-the-wild dataset with similar labels. This research introduces Triplet-based Domain Adaptation for Context REcognition (Triple-DARE), a lab-to-field neural network method that combines three unique loss functions to enhance intra-class compactness and inter-class separation within the embedding space of multi-labeled datasets: (1) domain alignment loss in order to learn domain-invariant embeddings; (2) classification loss to preserve task-discriminative features; and (3) joint fusion triplet loss. Rigorous evaluations showed that Triple-DARE achieved 6.3% and 4.5% higher F1-score and classification, respectively, than state-of-the-art HCR baselines and outperformed non-adaptive HCR models by 44.6% and 10.7%, respectively.

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