Wrist-worn Wearable Sensors to Understand Insides of the Human Body

Siirtola, Pekka; Peltonen, Ella; Koskimäki, Heli; Mönttinen, Henna; Röning, Juha; Pirttikangas, Susanna

doi:10.1145/3325424.3329663

Cited by 5 publications

(14 citation statements)

References 9 publications

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“…However, as the target value defined by the study subject was not reliable, most likely the estimations made by the models are closer to the truth than the target variables. Moreover, it is possible that people might not always know how they feel [ 43 , 44 ], and for this reason it seems that in certain cases the models are better at describing feelings than the study subjects themselves.…”

Section: Discussionmentioning

confidence: 99%

Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors

Siirtola

Tamminen

Chandra

et al. 2023

Sensors

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This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained from questionnaires. A variety of classification and regression models were compared, including Long Short-Term Memory (LSTM) models. Additionally, the effects of different normalization methods and the impact of using different sensors were studied, and the way in which the results differed between the study subjects was analyzed. The results revealed that regression models generally performed better than classification models, with LSTM regression models achieving the best results. The normalization method called baseline reduction was found to be the most effective, and when used with an LSTM-based regression model it achieved high accuracy in detecting valence (mean square error = 0.43 and R2-score = 0.71) and arousal (mean square error = 0.59 and R2-score = 0.81). Moreover, it was found that even if all biosignals were not used in the training phase, reliable models could be obtained; in fact, for certain study subjects the best results were obtained using only a few of the sensors.

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Section: Discussionmentioning

confidence: 99%

Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors

Siirtola

Tamminen

Chandra

et al. 2023

Sensors

Self Cite

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“…Data quality and signal distortion [109][110][111][112][113][114][115][116][117] Technical aspects related to wearable devices [109,114,118] User experience and behavior [112,114,118,119] Privacy [118] Interpretability [116] 3.4.1. Data Quality and Signal Distortion…”

Section: Theme Study(s)mentioning

confidence: 99%

“…Regarding signal distortion, studies have emphasized that raw data from wearable devices is often distorted and might not meet minimum standards for their use in real-world contexts. Among the most common causes of these distortions are motion artifacts [111,112,114,116], physical or human activity [109,114,116,117], and optical sensor issues [111]. Motion artifacts and human activity artifacts stem from the subject's movement during measurement, where the sensor may lose adherence (in the case of ECG or EDA) or move too far from the skin (in the case of PPG), resulting in instances of poor contact, and in some cases, complete loss of contact.…”

Section: Theme Study(s)mentioning

confidence: 99%

“…Focusing on the technical aspects related to wearable devices, the literature analysis has identified three limitations that affect real-world implementations of personalized stress detection models: battery life [109,114], computing and network capabilities [109], and vendor updates [118]. Battery life is the primary challenge when deploying personalized stress detection models that rely on biosignals from wearables.…”

Section: Technical Aspects Related To Wearable Devicesmentioning

confidence: 99%

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Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Bolpagni,

Pardini,

Dianti

et al. 2024

Preprint

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Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

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“…Recently, a few research studies have used self-supervised Signal steams from wearable devices are inherently lossy, resulting in gaps in signal streams [21]. Traditionally, researchers use statistical values such as mean and median values to replace the gaps in data [22].…”

Section: A Feature Engineering and Representation Learning For Emotio...mentioning

confidence: 99%

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

et al. 2022

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Extracting emotions from physiological signals has become popular over the past decade. Recent advancements in wearable smart devices have enabled capturing physiological signals continuously and unobtrusively. However, signal readings from different smart wearables are lossy due to user activities, making it difficult to develop robust models for emotion recognition. Also, the limited availability of data labels is an inherent challenge for developing machine learning techniques for emotion classification. This paper presents a novel self-supervised approach inspired by contrastive learning to address the above challenges. In particular, our proposed approach develops a method to learn representations of individual physiological signals, which can be used for downstream classification tasks. Our evaluation with four publicly available datasets shows that the proposed method surpasses the emotion recognition performance of state-of-the-art techniques for emotion classification. In addition, we show that our method is more robust to losses in the input signal.

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Wrist-worn Wearable Sensors to Understand Insides of the Human Body

Cited by 5 publications

References 9 publications

Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors

Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

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