Can smartphones detect stress-related changes in the behaviour of individuals?
Abstract
We describe initial results from an ongoing project to use mobile phone sensors to detect stress related situations. The questions that we address in this stage of our work in progress is whether differences between stressful and non stressful periods can be detected in information readily available on a smartphone such as location traces, BlueTooth devices seen during the day and phone call patterns. We present an experiment with 7 students who were monitored during a two week exam session (stressful situation) and the two following weeks (non stressful period). The results show that a behaviour modification can clearly be seen, although the exact interpretation and generalization requires further work and larger scale experiments.
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Mobile phones have been used to monitor stress and mental health.
“Mobile phones have been used to monitor stress and mental health [4-10].”
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Canzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477 · see citing works
“, [1, 10, 11, 29]) aim at identifying correlations between the collected data and the target parameter, whereas in our work we also propose predictive mechanisms in order to forecast depressive mood changes from mobility data.”
BackgroundMethodInfluentialStress detection in daily life scenarios using smart phones and wearable sensors: A survey
Can, Y. S., Arnrich, B., & Ersoy, C. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103139 · Cited by 439 · see citing works
Mental health monitoring with multimodal sensing and machine learning: A survey
Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Tørresen, J. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.09.003 · Cited by 356 · see citing works
Monitoring stress with a wrist device using context
Gjoreski, M., Luštrek, M., Gams, M., & Gjoreski, H. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.08.006 · Cited by 346 · see citing works
“Four Short STAI-Y anxiety questionnaires [26] were filled by each participant: before the experiment (1), and after the easy (2), medium (3) and hard session (4).”
BackgroundMethodSano, A., Taylor, S., McHill, A. W., Phillips, A. J., Barger, L. K., Klerman, E., & Picard, R. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9410 · Cited by 333 · see citing works
“Mobile phones have been used to monitor stress and mental health [4-10].”
MethodCan, Y. S., Chalabianloo, N., Ekiz, D., & Ersoy, C. (2019) · Italian National Conference on Sensors · doi:10.3390/s19081849 · Cited by 326 · see citing works
“Most works have only used features extracted from the smartphones [42–44].”
Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep
Muaremi, A., Arnrich, B., & Tröster, G. (2013) · BioNanoScience · doi:10.1007/s12668-013-0089-2 · Cited by 302 · see citing works
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“Other studies investigate the use of location information [5], measures of social interaction derived from phone-call, SMS, and proximity data [14] to detect stress.”
BackgroundGarcia-Ceja, E., Osmani, V., & Mayora, O. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2446195 · Cited by 267 · see citing works
“Using smartphones to monitor behaviour of subjects, we report the results of our study in detecting stress levels in real working environments.”
ResultDaily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654933 · Cited by 217 · see citing works
“verage behaviour modication of 53% for each user during the exam session. A limitation of this study is the small number of subjects. Our multifactorial approach outperforms the approach proposed by [1] although a direct comparison may be not adequate given the dierent focus: our approach tend to daily classify people…”
BackgroundMethodInfluentialWearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring
Sheikh, M., Qassem, M., & Kyriacou, P. A. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.662811 · Cited by 197 · see citing works
“Data tagging can be achieved through periodic specialist/doctor assessments on-site or by phone (66, 67), using self-reports often presented by a mobile application in periodic intervals in which the participant is in charge of the labeling process (68, 69) or according to some event, such as labeling stress during an…”
BackgroundDeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks
Suhara, Y., Xu, Y., & Pentland, A. '. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052676 · Cited by 163 · see citing works
“Previous studies have used individual physical activity information including behavioral, mobility, sleeping [38], voice acoustic [25][34], and social patterns [28][26][32] collected by pervasive computing devices to estimate a person’s mood, including depression [3][5][30][10][36][48][50], stress [3][4][12][39], anxi…”
BackgroundWearable Devices for Precision Medicine and Health State Monitoring
Jeong, I. C., Bychkov, D., & Searson, P. C. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2871638 · Cited by 155 · see citing works
“[240], [255], [260], credit card usage and various physical and physiological signals.”
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“Altogether, 67 full-text papers were read; of these, 16 were excluded because they did not relate sensor data to health status assessment [25-40], while another six were feasibility studies with no relation to health status assessment [41-46].”
BackgroundEMMA: an emotion-aware wellbeing chatbot
Ghandeharioun, A., McDuff, D., Czerwinski, M., & Rowan, K. (2019) · 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2019.8925455 · Cited by 126 · see citing works
“s, ranging from 55% to 80%. Examples include using smartphone data to model social interactions ([13]), to study the relationship between mood and sleep ([38]), to detect stress, happiness, and mood ([53, 5, 2, 29, 6, 24]), and to predict depressive symptoms ([50]). Others have also attempted prediction of ne grained…”
BackgroundPredicting students' happiness from physiology, phone, mobility, and behavioral data
Jaques, N., Taylor, S., Azaria, A., Ghandeharioun, A., Sano, A., & Picard, R. W. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344575 · Cited by 140 · see citing works
“Predicting stress from smartphone logs has been attempted by multiple researchers (e.g. [ 16] [17] [18]).”
BackgroundMethodPredicting Symptom Trajectories of Schizophrenia using Mobile Sensing
Wang, R., Wang, W., Aung, M. H., Ben‐Zeev, D., Brian, R., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J. M., Scherer, E. A., et al. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130976 · Cited by 105 · see citing works
“Other studies investigate the use of location information [3], measures of social interaction derived from phone calls, SMS, and proximity data [8] to detect stress.”
BackgroundState-of-the-Art of Stress Prediction from Heart Rate Variability Using Artificial Intelligence
Haque, Y., Zawad, R. S., Rony, C. S. A., Banna, H. A., Ghosh, T., Kaiser, M., & Mahmud, M. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10200-0 · Cited by 105 · see citing works
“Behavioural patterns, as well as physiological data (GSR, EEG, HR), can be collected through smart-phones, and by combining these sensor data and smart-phone records (calls, locations), stress can be predicted [8].”
Sağbaş, E. A., Korukoglu, S., & Balli, S. (2020) · Journal of medical systems · doi:10.1007/s10916-020-1530-z · Cited by 89 · see citing works
Smartphone app usage as a predictor of perceived stress levels at workplace
Ferdous, R., Osmani, V., & Mayora, O. (2015) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2015.260192 · Cited by 86 · see citing works
Predicting anxiety state using smartphone-based passive sensing
Fukazawa, Y., Ito, T., Okimura, T., Yamashita, Y., Maeda, T., & Ota, J. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103151 · Cited by 81 · see citing works
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“However, since outside classroom environments are not controlled as the mentioned restricted environments, campus stress detection studies could be regarded as a bridge from restricted environments to unlimited daily life environments [33], [34], [35].”
BackgroundAutomatic Detection of Perceived Stress in Campus Students Using Smartphones
Gjoreski, M., Gjoreski, H., Lutrek, M., & Gams, M. (2015) · Australasian Conference on Interactive Entertainment · doi:10.1109/ie.2015.27 · Cited by 75 · see citing works
“[11] focused on detecting stressrelated changes in the subject’s behavior.”
BackgroundA Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors
Kołakowska, A., Szwoch, W., & Szwoch, M. (2020) · Italian National Conference on Sensors · doi:10.3390/s20216367 · Cited by 66 · see citing works
“In the case of large amounts of training data, a proper choice is to find personalised feature subsets, as different features prove to have discriminative power for different users [56,57,60,61].”
BackgroundMethodInfluentialMulti-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression
Farhan, A. A., Lu, J., Bi, J., Russell, A., Wang, B., & Bamis, A. (2016) · 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1109/chase.2016.27 · Cited by 61 · see citing works
“In some other studies, smartphones have been used to monitor sleep [8], stress [3], [5], [15], [24], mood [13], and general wellbeing [14], [22].”
BackgroundStress modelling and prediction in presence of scarce data
Maxhuni, A., Hernandez-Leal, P., Sucar, L., Osmani, V., Morales, E., & Mayora, O. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.08.023 · Cited by 61 · see citing works
“Another relevant work is from Bauer and Lukowicz [22] whose work aimed at recognizing stress from 7 students before and after the exam period.”
BackgroundMonitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors
Muaremi, A., Bexheti, A., Gravenhorst, F., Arnrich, B., & Troster, G. (2014) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) · doi:10.1109/bhi.2014.6864335 · Cited by 60 · see citing works
“”MoodSense” [10] and a similar work, [11], try to infer stress-related changes in users’ behavior through their smartphone usage patterns and location information.”
BackgroundNew Methods for Stress Assessment and Monitoring at the Workplace
Carneiro, D. R., Nováis, P., Augusto, J. C., & Payne, N. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2699633 · Cited by 63 · see citing works
“Although not in a conclusive manner, in [76] the authors find significant differences”
ResultInvestigating causality in human behavior from smartphone sensor data: a quasi-experimental approach
Tsapeli, F., & Musolesi, M. (2015) · EPJ Data Science · doi:10.1140/epjds/s13688-015-0061-1 · Cited by 54 · see citing works
Pervasive stress recognition for sustainable living
Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (2014) · 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS) · doi:10.1109/percomw.2014.6815230 · Cited by 53 · see citing works
“The system achieved an initial 66% accuracy of participants’ daily mood, gradually improving to 93% after two months of training, with phone calls and categorized applications being the most relevant features.”
BackgroundiSensestress: Assessing stress through human-smartphone interaction analysis
Ciman, M., Wac, K., & Gaggi, O. (2015) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2015.259280 · Cited by 52 · see citing works
“Bauer and Lukowicz [6] researched behavior change during stress and no-stress period using a smartphone, leveraging data about the location of the user (using GPS and WiFi), his/her social interactions (using Bluetooth sensor), and calls/SMSes patterns.”
Aristizabal, S., Byun, K., Wood, N. G., Mullan, A. F., Porter, P., Campanella, C., Jamrozik, A., Nenadic, I., & Bauer, B. (2021) · IEEE Access · doi:10.1109/access.2021.3097038 · Cited by 50 · see citing works
“, Electrodermal activity (EDA), photoplethysmography (PPG), and acceleration sensors) to measure physiological signs of stress [32], [36]–[40].”
BackgroundMeasuring the Effects of Stress on Mobile Interaction
Sarsenbayeva, Z., van Berkel, N., Hettiachchi, D., Jiang, W., Dingler, T., Velloso, E., Kostakos, V., & Gonçalves, J. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3314411 · Cited by 48 · see citing works
“%) that only used smartphone interaction patterns [3].”
BackgroundReal-Life Stress Level Monitoring Using Smart Bands in the Light of Contextual Information
Can, Y. S., Chalabianloo, N., Ekiz, D., Fernández-Álvarez, J., Repetto, C., Riva, G., Iles-Smith, H., & Ersoy, C. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2984644 · Cited by 44 · see citing works
Smartphone Based Stress Prediction
Stütz, T., Kowar, T., Kager, M., Tiefengrabner, M., Stuppner, M., Blechert, J., Wilhelm, F., & Ginzinger, S. W. (2015) · User Modeling, Adaptation, and Personalization · doi:10.1007/978-3-319-20267-9_20 · Cited by 44 · see citing works
Analyzing and Identifying Teens’ Stressful Periods and Stressor Events From a Microblog
Li, Q., Xue, Y., Zhao, L., Jia, J., & Feng, L. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2586519 · Cited by 52 · see citing works
“In [8], smart phones were used as a voice sensor by collecting voice variations when people talking naturally, focusing on cognitive stress and stressor frequency estimation.”
BackgroundMethodA Presence-Based Context-Aware Chronic Stress Recognition System
Peternel, K., Pogačnik, M., Tavčar, R., & Kos, A. (2012) · Sensors · doi:10.3390/s121115888 · Cited by 35 · see citing works
“Bauer and Lukowicz [15] have recently described some initial results from an ongoing project.”
BackgroundMedically-oriented design for explainable AI for stress prediction from physiological measurements
Jaber, D., Hajj, H. M., Maalouf, F., & El-Hajj, W. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01772-2 · Cited by 39
“When those data were combined with global positioning system (GPS) and Wi-Fi information, these features used allowed to detect a change of behavior in about 86% of the participants during stressful times [26].”
MethodNoninvasive stress recognition considering the current activity
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“[12] GPS, WiFi, social interaction (Bluetooth), call and sms log No No 53 % behavior modification”
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“Researches have also considered smartphones to monitor stress by analyzing user behavior [20, 23].”
BackgroundEstimation of the Driving Style Based on the Users’ Activity and Environment Influence
Sysoev, M., Kos, A., Guna, J., & Pogacnik, M. (2017) · Italian National Conference on Sensors · doi:10.3390/s17102404 · Cited by 31 · see citing works
A survey on mobile affective computing
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Affective State Prediction Based on Semi-Supervised Learning from Smartphone Touch Data
Wampfler, R., Klingler, S., Solenthaler, B., Schinazi, V. R., & Groß, M. H. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376504 · Cited by 31 · see citing works
“Most existing approaches for stress prediction used two [7, 50, 5] or three classes [53], and the stress measurement tool of choice were self-reports [24, 53, 19].”
BackgroundMethodInfluentialPassive social sensing with smartphones: a systematic review
Zhang, H., Ibrahim, A., Parsia, B., Poliakoff, E., & Harper, S. (2022) · Computing · doi:10.1007/s00607-022-01112-2 · Cited by 29 · see citing works
“Fifteen studies set fixed sample rates for recording data: 10 [48], 15 [25, 53], 20 [51] or 60 minutes [37, 50] for GPS; 3 [42, 43], 5 [27, 28, 30, 46], 6 [35] or 10 minutes [51] for Bluetooth; 6 [35], 15 [25] or 60 minutes [50]for Wi-Fi; and 3 seconds [51] or 3 minutes [47, 54] for microphones.”
BackgroundInfluentialUnobtrusive Stress Assessment Using Smartphones
Maxhuni, A., Hernandez-Leal, P., Morales, E., Sucar, L. E., Osmani, V., & Mayora, O. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.2974834 · Cited by 25 · see citing works
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“Stress-related changes in human behavior using GPS, WiFi, Bluetooth, phone calls, and SMS logs features of a smartphone are explored in [458].”
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“It was decided to design the interface upon a geospatial framework as the use of location is a common framework for lifelogging systems that seek to associate instances of stress with a specific place (Bauer and Lukowicz, 2012; Sano and Picard, 2013; Likamwa et al., 2013; Vhaduri et al., 2014; Bogomolov et al., 2014;…”
BackgroundA Survey on Mobile Social Signal Processing
Palaghias, N., Hoseinitabatabaei, S. A., Nati, M., Gluhak, A., & Moessner, K. (2016) · ACM Computing Surveys · doi:10.1145/2893487 · Cited by 17 · see citing works
Pervasive Healthcare-A Comprehensive Survey of Tools and TechniquesPreprint
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“Smartphones are very useful [Bauer and Lukowicz 2012] for monitoring stress related changes in human behavior through different embedded sensor nodes.”
BackgroundHealthwalks: sensing fine-grained individual health condition via mobility data
Lin, Z., Lyu, S., Cao, H., Xu, F., Wei, Y., Samet, H., & Li, Y. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432229 · Cited by 18 · see citing works
“User locations and social connections are exploited by [31] to detect the stressful periods.”
BackgroundThe role of non-intrusive approaches in the development of people-aware systems
Novais, P., & Carneiro, D. (2016) · Progress in Artificial Intelligence · doi:10.1007/s13748-016-0085-1 · Cited by 18 · see citing works
“On a purely behavioral approach, Bauer and Lukowicz [4] describe how changes in the behavior of the users of a smartphone can be detected to classify stress.”
BackgroundTowards Real-Time Automatic Stress Detection for Office Workplaces
Suni-Lopez, F., Condorí-Fernández, N., & Catalá, A. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-11680-4_27 · Cited by 32 · see citing works
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“Smartphone interaction data were used by Bauer and Lukowicz (2012), Ciman et al. (2015), and Sano and Picard (2013) to identify users’ stress level for healthcare applications.”
MethodEMMA: An Emotionally Intelligent Personal Assistant for Improving Wellbeing
Ghandeharioun, A., McDuff, D. J., Czerwinski, M., & Rowan, K. (2018) · arXiv.org · doi:10.48550/arxiv.1812.11423 · Cited by 14
“s, ranging from 55% to 80%. Examples include using smartphone data to model social interactions ([13]), to study the relationship between mood and sleep ([38]), to detect stress, happiness, and mood ([53, 5, 2, 29, 6, 24]), and to predict depressive symptoms ([50]). Others have also attempted prediction of ne grained…”
BackgroundBravo, J., Hervás, R., & Villarreal, V. (2015) · International Conference on Ambient Intelligence for Health · doi:10.1007/978-3-319-26508-7 · Cited by 14
Deep Learning Framework for Classification of Mental Stress from Multimodal Datasets
Shermadurai, P., & Thiyagarajan, K. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370119 · Cited by 12 · see citing works
“The decision tree classifier with an ECG signal had the most significant classification accuracy in a campus setting for two-class categorization [44].”
Crivellari, A., & Beinat, E. (2019) · Applied Sciences · doi:10.3390/app9142861 · Cited by 29 · see citing works
“Other works use human mobility data to assess the users’ physical and mental health conditions, such as to predict flu-like symptoms [14], daily mood states [15], and stress levels [16].”
BackgroundMobileSSI: asynchronous fusion for social signal interpretation in the wild
Flutura, S., Wagner, J., Lingenfelser, F., Seiderer, A., & André, E. (2016) · International Conference on Multimodal Interaction · doi:10.1145/2993148.2993164 · Cited by 12 · see citing works
“In the literature, a wide range of features has been investigated including the amount of conversation recorded by the smartphone’s microphone [5], communication data [14], postures [10] or proximity behaviors detected by Bluetooth patterns [2].”
BackgroundCan your smartphone detect your emotion?
Dai, D., Liu, Q., & Meng, H. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery · doi:10.1109/fskd.2016.7603434 · Cited by 12 · see citing works
“proposed a method to detect stress-related changes in the behavior of individuals by using smartphones [16].”
MethodInvestigating correlation between verbal interactions and perceived stress
Ferdous, R., Osmani, V., Beltrán-Márquez, J., & Mayora, O. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2015.7318683 · Cited by 12 · see citing works
“A number of authors [10]–[13] have investigated correlation between human behaviour and perceived stress at work.”
BackgroundEmotion Classification from Noisy Speech - A Deep Learning Approach
Rana, R. (2016) · arXiv.org · doi:10.48550/arxiv.1603.05901 · Cited by 11
“The reconstruction and the propagation passes are repeated adjusting the weights to minimise reconstruction error.”
BackgroundStress Modelling Using Transfer Learning in Presence of Scarce Data
Hernandez-Leal, P., Maxhuni, A., Sucar, L. E., Osmani, V., Morales, E., & Mayora, O. (2015) · International Conference on Ambient Intelligence for Health · doi:10.1007/978-3-319-26508-7_22 · Cited by 11 · see citing works
“[12] whose work aimed at recognizing stress from 7 students before and after the exam period.”
BackgroundPassive Health Monitoring Using Large Scale Mobility Data
Zhang, Y., Xu, F., Li, T., Kostakos, V., Hui, P., & Li, Y. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448078 · Cited by 10 · see citing works
“Health-related apps can utilize the data to accurately predict the likelihood of users requiring hospital treatment, then raise timely warnings to the user.”
BackgroundPerson and Stressor Independent Generic Model for Stress Detection Using GSR
Jaiswal, D., Chatterjee, D., Gavas, R., Ramakrishnan, R. K., & Pal, A. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc46164.2021.9630615 · Cited by 10
“After executing the task, participants reported their self-perceived stress level using Short STAI-Y anxiety questionnaires [22].”
MethodAnalysis of Teens’ Chronic Stress on Micro-blog
Xue, Y., Li, Q., Zhao, L., Jia, J., Feng, L., Yu, F., & Clifton, D. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48743-4_10 · Cited by 10 · see citing works
“Smart phones were also used as a kind of sensor to detect people’s mental stress by analyzing their voice variation in diverse conversational situations [10,11].”
BackgroundMethodUsing Intermediate Models and Knowledge Learning to Improve Stress Prediction
Maxhuni, A., Hernandez-Leal, P., Morales, E., Sucar, L. E., Osmani, V., Muñoz-Meléndez, A., & Mayora, O. (2017) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-49622-1_16 · Cited by 10 · see citing works
“Bauer et al.[15] Smartphone (Location, bluetooth, phone-call and SMS) Uncontrolled Medium”
BackgroundHow smart your smartphone is in lie detection?
Rahman, M. M., Shome, A., Chellappan, S., & Islam, A. A. (2019) · International Conference on Mobile and Ubiquitous Systems: Networking and Services · doi:10.1145/3360774.3360788 · Cited by 9
A Minimalist Approach for Identifying Affective States for Mobile Interaction Design
Tikadar, S., Kazipeta, S., Ganji, C., & Bhattacharya, S. (2017) · IFIP TC13 International Conference on Human-Computer Interaction · doi:10.1007/978-3-319-67744-6_1 · Cited by 9 · see citing works
“Sano and Picard [30], Bauer and Lukowicz [2] and Ciman et al [4] used user-smartphone interaction data to detect the users’ sate.”
BackgroundMethodDetecting Anxiety States when Caring for People with Dementia
Miranda, D., Favela, J., & Arnrich, B. (2016) · Methods of Information in Medicine · doi:10.3414/me15-02-0012 · Cited by 9 · see citing works
Smartphone based experience sampling of stress-related events
Weppner, J., Lukowicz, P., Serino, S., Cipresso, P., Gaggioli, A., & Riva, G. (2013) · 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops · doi:10.4108/icst.pervasivehealth.2013.252358 · Cited by 9 · see citing works
“An initial experiment was performed previously were the location, social interaction, telephone call behavior and text message behavior was compared between a two week stressful situation and a two week non-stressful situation [9] with the result of an average behavior modification of 53%.”
Self-citationUnobtrusive stress detection using wearables: application and challenges in a university setting
Neigel, P., Vargo, A. W., Tag, B., & Kise, K. (2025) · Frontiers Comput. Sci. · doi:10.3389/fcomp.2025.1575404 · Cited by 8 · see citing works
Smartphone apps usage patterns as a predictor of perceived stress levels at workplacePreprint
Ferdous, R., Osmani, V., & Mayora, O. (2018) · arXiv.org · doi:10.48550/arxiv.1803.03863 · Cited by 8 · see citing works
“A number of authors [17]–[19] have investigated correlation between human behaviour and perceived stress at work by analysing the data derived from the smartphones data or sensors data.”
BackgroundUsing Wearables to Unobtrusively Identify Periods of Stress in a Real University Environment
Neigel, P., Vargo, A. W., Tag, B., & Kise, K. (2024) · International Workshop on the Semantic Web · doi:10.1145/3675095.3676608 · Cited by 7 · see citing works
“Alternatively, contextual information, e.g., events such as exams, can be used to infer stress levels [6, 7, 27].”
Graph attention networks unveil determinants of intra- and inter-city health disparity
Liu, C., Fan, C. H., & Mostafavi, A. (2024) · Urban Informatics · doi:10.1007/s44212-024-00049-5 · Cited by 9 · see citing works
“( Bauer & Lukowicz, 2012) describes the initial results of an ongoing project to use mobile phone sensors to detect stress-related situations.”
Vega, J., Bell, B. T., Taylor, C. S., Xie, J., Ng, H., Honary, M., & McNaney, R. (2022) · JMIR Mental Health · doi:10.2196/32146 · Cited by 6 · see citing works
“Research has shown the potential of these data to monitor and support mental health conditions, including depression [15,38,41-44], schizophrenia [13,45-48], bipolar disorder [14,49-53], stress [54-58] and anxiety [59-62].”
Differences in smartphone usage: Validating, evaluating, and predicting mobile user intimacy
Gustarini, M., Scipioni, M. P., Fanourakis, M. A., & Wac, K. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.06.003 · Cited by 7 · see citing works
Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly
Matsumoto, K., Matsui, T., Suwa, H., & Yasumoto, K. (2023) · Italian National Conference on Sensors · doi:10.3390/s23010535 · Cited by 5 · see citing works
“The second group of studies analyzes stress in real life [12,34,35] and have reported relatively low accuracy [36].”
BackgroundA communication and multi-sensor solution to support dynamic generation of a situational picture
Umlauft, M., Raffelsberger, C., Kercek, A., Almer, A., Schnabel, T., Luley, P., & Ladstaetter, S. (2016) · International Conference on Information and Communication Technologies for Disaster Management · doi:10.1109/ict-dm.2016.7857205 · Cited by 5
“Communication, localization , and detection of the user and environment context during a mission need to be considered for optimal adaptation of the system [4], [5].”
BackgroundSmartphone-based Qualitative Analyses of Social Activities During Family Time
Sahiti, K., Kalanadhabhatta, M., Bhunia, S., Singhal, A., & Majethia, R. (2017) · HumanSys@SenSys · doi:10.1145/3144730.3144735 · Cited by 4 · see citing works
“Such work has been improved upon by targeting specific interest groups, such as students, or the elderly, so that certain social activities and their effect on mental well being can be effectively studied, such as in [20] and [2].”
BackgroundTeenSensor: ?Gaussian processes for micro-blog based Teen's acute and chronic stress detection
Xue, Y.-Y., Li, Q., & Feng, L. (2019) · Computer systems science and engineering · doi:10.32604/csse.2019.34.151 · Cited by 4
“Smart phones as a kind of speech sensors were also exploited in [13, 14], focusing on cognitive stress and stressor frequency estimation rather than the type and severity of stress.”
BackgroundMethodDeep learning to segment first-time and repeat travelers: Analyzing GPS data
Yuan, Y., & Park, S. (2025) · Annals of Tourism Research · doi:10.1016/j.annals.2025.104060 · Cited by 4 · see citing works
Personalized Neural Modeling for Daily Injury Risk Assessment via Wearable Health Data
Ozolcer, M., & Bae, S. W. (2025) · IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3721201.3724417 · Cited by 3 · see citing works
“Parallel to these hardware advances, data-driven approaches have shifted from simple step counts and heart-rate averages toward richer contextual integrations involving smartphone usage logs [13], self-reported mental states [2, 8], and mobility patterns predictive of clinical outcomes [1, 9].”
Predicting Volleyball Season Performance Using Pre-Season Wearable Data and Machine Learning
Ozolcer, M., Zhang, T.-Z., & Bae, S. W. (2025) · International Conference on Activity and Behavior Computing · doi:10.1109/abc64332.2025.11118608 · Cited by 3
“Prior research has examined health monitoring through collecting mobile app usage data to infer objective and subjective well-being, and mental health, [28]–[30].”
Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition
Qi, Y., Jia, W.-C., Feng, L., Dai, Y.-N., Tang, C.-Y., Zhou, F.-Q., & Gao, S. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3392569 · Cited by 3 · see citing works
“With a piezoelectric touch panel, fi nger press and release actions generate positive and negative impulses, respectively [70].”
BackgroundLee, H., Park, T., Koh, Y., Lee, J.-G., & Lee, U. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3770644 · Cited by 2 · see citing works
Cognitive performance measurements and the impact of sleep quality using wearable and mobile sensors
Visuri, A., Koskimäki, H., van Berkel, N., Alorwu, A., Peltonen, E., Abdullah, S., & Hosio, S. J. (2025) · Personal and Ubiquitous Computing · doi:10.1007/s00779-025-01847-7 · Cited by 4 · see citing works
“Smartphone use is frequently used in Human-Computer Interaction studies to better understand human behaviour through interactions with the smartphone [59], responses to interruptions [60], biological processes [61, 62], mental statessuchasstress[63,64],anddepression[65].”
Identifying Periods of Cyclical Stress in University Students Using Wearables In-the-Wild
Neigel, P., Vargo, A. W., Tag, B., & Kise, K. (2024) · arXiv.org · doi:10.48550/arxiv.2402.11823 · Cited by 2
“[5 ] use an approach similar to ours: They consider a two week exam period as a stressful time and the following two weeks as stress-free and try to distinguish between them based on location traces, Bluetooth device connections, SMS and phone call patterns of 7 participants.”
MethodAnalysing smartphone users “inner-self”: the perception of intimacy and smartphone usage changes
Gustarini, M. (2016) · doi:10.13097/archive-ouverte/unige:85031 · Cited by 2
“As in this case with intimacy, other researchers considered the importance of subjective context elements, for example: detecting users’ mood [4] or stress [38] states, and relate them to different smartphone usage patterns.”
BackgroundEnergy-efficient Continuous Context Sensing on Mobile Phones
Eberle, J. (2015) · doi:10.5075/epfl-thesis-6761 · Cited by 2
“Based on the mobility traces, call logs and the social context from Bluetooth scans, Bauer and Lukowicz [19] were able to show a significant change in participant’s behavior between stressful and non stressful situations.”
MethodAssessing Stress with Mobile Systems: A Design Science Approach
Bonenberger, L., Gimpel, H., Regal, C., & Schmidt, M. (2023) · European Journal of Information Systems · doi:10.1080/0960085x.2023.2295313 · Cited by 1 · see citing works
“MSA systems can use non-personal data from user environments (Betti et al., 2018), aggregated personal data (e.g., number or duration of phone calls; Bauer & Lukowicz, 2012), or raw personal data.”
Optimizing Datasets in the E-Learning Ecosystem A State-of-the-Art Review
Elbazi, A., Mohamed, C., & Abdelhamid, B. (2023) · 2023 XIII International Conference on Virtual Campus (JICV) · doi:10.1109/jicv59748.2023.10565650 · Cited by 1
“…and Support Vector Machines (SVMs) classify learners into categories, facilitating tailored content recommendations and interventions based on shared characteristics [15] H. Ensemble Methods Ensemble methods combine predictions from multiple algorithms to improve accuracy and robustness.”
BackgroundContinuous Stress Detection Based on Social Media
Ding, Y., Feng, L., Cao, L., Dai, Y. Q., Wang, X., Zhang, H., Li, N., & Zeng, K. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3283338 · Cited by 4 · see citing works
Ramirez-Valenzuela, R. A., Monroy, R., Loyola-González, O., Godínez, F., & Soberanes-Martín, A. (2021) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-021-00765-1 · Cited by 1 · see citing works
Towards using situational information to detect an individual's perceived stress level
Neitzel, S., Englert, F., Dwarakanath, R. C., Schneider, K., Reinke, K., Gerlach, G. I., Rensing, C., Boehnstedt, D., & Stock‐Homburg, R. (2017) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2017.7917584
“Furthermore, mobility patterns [19], communication patterns [15], [19] and phone usage patterns [16] have been used to detect an individual’s emotions and mood.”
MethodAffect Sensing on Smartphone - Possibilities of Understanding Cognitive Decline in Aging PopulationPreprint
Rana, R., Reilly, J., Jurdak, R., Hu, W., Li, X., & Soar, J. (2014) · arXiv.org · doi:10.48550/arxiv.1407.5910 · Cited by 1 · see citing works
“cation was used in lieu of accuracy [29].”
MethodCarvalho, C., Torres, J. M., & Moreira, R. S. (2026) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3776574.3831116
“[8] [FAWT] splits a signal into sub-bands [25]; [-IMGBKG] example: background removal for shoulder slope calculation [78]; [CVXEDA] algorithm used to process/analyze (EDA) signal [23]; [IDP] automatic speech recognition feature extraction technique [54]; [LBF] used to extract facial landmark points…”
Detection of social connectedness in everyday life via multimodal lifelogging data
Dobbins, C., Fairclough, S. H., Haslam, C., Haslam, S. A., & Bentley, S. V. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103749
Björnfot, P., & Kaptelinin, V. (2025) · European Conference on Cognitive Ergonomics · doi:10.1145/3746175.3746201
“Additionally, behavioral patterns, such as smartphone usage, can provide valuable measurements [6].”
User Behavioral Characteristics Identification from Mobile Call Logs
Paritala, S., Bobba, R., Akkineni, H., Papineni, V. S. L., & Gogulamudi, L. (2018) · Smart Intelligent Computing and Applications · doi:10.1007/978-981-13-1921-1_66
Personalising and crowdsourcing stress management in urban environments via s-Health
Papageorgiou, A., Zigomitros, A., & Patsakis, C. (2015) · International Conference on Information, Intelligence, Systems and Applications · doi:10.1109/iisa.2015.7388027
“There are several studies focusing on the of use smart devices to examine stress levels [11], [10], [12], or the relation between stress and daily habits or activities of users that can affect their mood [13], [14], [15].”
Background
- Authors
- Bauer, G., & Lukowicz, P.
- Affiliations
- University of Passau (DE)
- ORCID
- Lukowicz, Paul
- Venue
- 2012 IEEE International Conference on Pervasive Computing and Communications Workshops
- Publisher
- IEEE
- Published
- 2012-03
- Pages
- 423-426
- OpenAlex
- W2068349382
- Cited by
- 111
- References
- 0
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 3 · 2023: 9 · 2022: 7 · 2021: 11 · 2020: 10 · 2019: 10 · 2018: 7 · 2017: 9 · 2016: 16 · 2015: 12 · 2014: 6 · 2013: 3 · 2012: 1
- FWCI
- 5.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- stress detection, smartphone sensors, mobile phone tracking, location traces, Bluetooth devices, behavior modification, exam stress, stress behavior
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