SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

Irshad, Muhammad Tausif; Nisar, Muhammad Adeel; Huang, Xinyu; Hartz, Jana; Flak, Olaf; Li, Frédéric; Gouverneur, Philip; Piet, Artur; Oltmanns, Kerstin M.; Grzegorzek, Marcin

doi:10.3390/s22207711

Cited by 18 publications

(9 citation statements)

References 58 publications

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“…Considering that objectively collecting trigger-related data or self-assessment data on health symptoms from a mobile app may currently be challenging as triggers (such as food cravings and hunger) are physiologically motivated, future studies on BCSSs that seek to extract trigger-related data may consider using physiological sensor data. This is a noninvasive approach to detecting hunger and cravings using wearable body sensors [ 39 ]. Such sensor data may also be integrated into the health app to enable self-monitoring.…”

Section: Resultsmentioning

confidence: 99%

Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review

Ekpezu,

Wiafe,

Oinas-Kukkonen

2023

JMIR AI

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Background There is a dearth of knowledge on reliable adherence prediction measures in behavior change support systems (BCSSs). Existing reviews have predominately focused on self-reporting measures of adherence. These measures are susceptible to overestimation or underestimation of adherence behavior. Objective This systematic review seeks to identify and summarize trends in the use of machine learning approaches to predict adherence to BCSSs. Methods Systematic literature searches were conducted in the Scopus and PubMed electronic databases between January 2011 and August 2022. The initial search retrieved 2182 journal papers, but only 11 of these papers were eligible for this review. Results A total of 4 categories of adherence problems in BCSSs were identified: adherence to digital cognitive and behavioral interventions, medication adherence, physical activity adherence, and diet adherence. The use of machine learning techniques for real-time adherence prediction in BCSSs is gaining research attention. A total of 13 unique supervised learning techniques were identified and the majority of them were traditional machine learning techniques (eg, support vector machine). Long short-term memory, multilayer perception, and ensemble learning are currently the only advanced learning techniques. Despite the heterogeneity in the feature selection approaches, most prediction models achieved good classification accuracies. This indicates that the features or predictors used were a good representation of the adherence problem. Conclusions Using machine learning algorithms to predict the adherence behavior of a BCSS user can facilitate the reinforcement of adherence behavior. This can be achieved by developing intelligent BCSSs that can provide users with more personalized, tailored, and timely suggestions.

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Section: Resultsmentioning

confidence: 99%

Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review

Ekpezu,

Wiafe,

Oinas-Kukkonen

2023

JMIR AI

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“…This section presents details on the sensor modalities that were used for data acquisition, discusses the data acquisition process, and explains the experimental settings. Figure 1 shows all the steps in the process from data acquisition to evaluation, which has been extensively described in [38,59,60].…”

Section: Methodsmentioning

confidence: 99%

“…To recognize the driving conditions, we had to obtain a huge amount of data. In this article, we will describe how the data and basic laws of physics were incorporated into the sensors in JINS MEME ES_R glasses) and how the data were obtained for analysis and classification [34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec¹,

Konior²,

Sieciński³

et al. 2023

Preprint

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To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary activities related to activities of daily life and secondary when driving a car.

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“…In many studies [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], feature engineering algorithms such as Fourier Transform (FT), Wavelet Transform (WT), Spectral Features Analysis (SFA), and Time-frequency Analysis (TA), etc., were used to generate and extract hand-crafted features from PSG recordings. Then various machine learning methods (e.g., Support Vector Machine (SVM), Decision Tree (DT), Adaptive Boosting (Adaboost) and RF, etc.)…”

Section: Related Workmentioning

confidence: 99%

Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation

Huang

Shirahama

Irshad

et al. 2023

Sensors

Self Cite

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The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset—Sleep-EDFX—to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data.

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SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

Cited by 18 publications

References 58 publications

Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review

Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation

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