{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:23:58Z","timestamp":1787919838152,"version":"build-2784847793"},"reference-count":21,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Intelligent Society Platform (INSO)","award":["328226"],"award-info":[{"award-number":["328226"]}]},{"name":"The Intelligent Society Platform (INSO)","award":["328214"],"award-info":[{"award-number":["328214"]}]},{"name":"Academy of Finland, under the project ULTRA","award":["328226"],"award-info":[{"award-number":["328226"]}]},{"name":"Academy of Finland, under the project ULTRA","award":["328214"],"award-info":[{"award-number":["328214"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the most important aspect which could impact feelings of loneliness is mobility. In this paper, we present a machine-learning based approach to classify the user loneliness levels using their indoor and outdoor mobility patterns. User mobility data has been collected based on indoor and outdoor sensors carried on by volunteers frequenting an elderly nursing house in Tampere region, Finland. The data was collected using Pozyx sensor for indoor data and Pico minifinder sensor for outdoor data. Mobility patterns such as the distance traveled indoors and outdoors, indoor and outdoor estimated speed, and frequently visited clusters were the most relevant features for classifying the user\u2019s perceived loneliness levels.Three types of data used for classification task were indoor data, outdoor data and combined indoor-outdoor data. Indoor data consisted of indoor mobility data and statistical features from accelerometer data, outdoor data consisted of outdoor mobility data and other parameters such as speed recorded from sensors and course of a person whereas combined indoor-outdoor data had common mobility features from both indoor and outdoor data. We found that the machine-learning model based on XGBoost algorithm achieved the highest performance with accuracy between 90% and 98% for indoor, outdoor, and combined indoor-outdoor data. We also found that Lubben-scale based labelling of perceived loneliness works better for both indoor and outdoor data, whereas UCLA scale-based labelling works better with combined indoor-outdoor data.<\/jats:p>","DOI":"10.3390\/s22134946","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T01:40:36Z","timestamp":1656639636000},"page":"4946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Machine-Learning-Based Analysis of the Relationships between Loneliness Metrics and Mobility Patterns for Elderly"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9802-2061","authenticated-orcid":false,"given":"Aditi","family":"Site","sequence":"first","affiliation":[{"name":"Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4180-7674","authenticated-orcid":false,"given":"Saigopal","family":"Vasudevan","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8132-3017","authenticated-orcid":false,"given":"Samuel Olaiya","family":"Afolaranmi","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6227-3408","authenticated-orcid":false,"given":"Jose L. Martinez","family":"Lastra","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-4606","authenticated-orcid":false,"given":"Jari","family":"Nurmi","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-6924","authenticated-orcid":false,"given":"Elena Simona","family":"Lohan","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"ref_1","first-page":"602","article-title":"Predicting loneliness in older age using two measures of loneliness","volume":"42","author":"Yang","year":"2020","journal-title":"Int. J. Comput. Appl."},{"key":"ref_2","first-page":"410","article-title":"Developing an IoT Platform for the Elderly Health Care","volume":"12","author":"Awadalla","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Site, A., Lohan, E.S., Jolanki, O., Valkama, O., Hernandez, R.R., Latikka, R., Alekseeva, D., Vasudevan, S., Afolaranmi, S., and Ometov, A. (2022). Managing Perceived Loneliness and Social-Isolation Levels for Older Adults: A Survey with Focus on Wearables-Based Solutions. Sensors, 22.","DOI":"10.3390\/s22031108"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e28022","DOI":"10.2196\/28022","article-title":"Older Adults\u2019 Loneliness, Social Isolation and Physical ICT in the Era of Ambient Assisted Living: A Systematic Literature Review","volume":"23","author":"Latikka","year":"2021","journal-title":"J. Med Internet Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112221","DOI":"10.1109\/ACCESS.2021.3103268","article-title":"Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis","volume":"9","author":"Site","year":"2021","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jeewandara, J., Karunaratne, L., and Sanju, K. (2021, January 7\u20138). An Efficient Machine Learning Based Elderly Monitoring System. Proceedings of the 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Mauritius, Mauritius.","DOI":"10.1109\/ICECCME52200.2021.9591079"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bergefurt, L., Kemperman, A., van den Berg, P., Borgers, A., van der Waerden, P., Oosterhuis, G., and Hommel, M. (2019). Loneliness and Life Satisfaction Explained by Public-Space Use and Mobility Patterns. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16214282"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1186\/s40814-020-00587-0","article-title":"Physical Activity Intervention for Loneliness (PAIL) in community-dwelling older adults: A randomised feasibility study","volume":"6","author":"Shvedko","year":"2020","journal-title":"Pilot Feasibility Stud."},{"key":"ref_9","unstructured":"(2022, June 14). AISOLA (Implementation of Contextual Complexity in AI-Based Assessment Systems of Older People\u2019s Social Isolation). Available online: https:\/\/projects.tuni.fi\/aisola\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1093\/geront\/46.4.503","article-title":"Performance of an abbreviated version of the Lubben Social Network Scale among three European community-dwelling older adult populations","volume":"46","author":"Lubben","year":"2006","journal-title":"Gerontologist"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1037\/0022-3514.39.3.472","article-title":"The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence","volume":"39","author":"Russell","year":"1980","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1177\/0164027504268574","article-title":"A short scale for measuring loneliness in large surveys: Results from two population-based studies","volume":"26","author":"Hughes","year":"2004","journal-title":"Res. Aging"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dabove, P., Di Pietra, V., Piras, M., Jabbar, A.A., and Kazim, S.A. (2018, January 23\u201326). Indoor positioning using Ultra-wide band (UWB) technologies: Positioning accuracies and sensors\u2019 performances. Proceedings of the 2018 IEEE\/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA.","DOI":"10.1109\/PLANS.2018.8373379"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Plachta, K. (2018, January 12\u201315). Autonomous tracking controller for photovoltaic systems using global positioning system. Proceedings of the 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC\/I&CPS Europe), Palermo, Italy.","DOI":"10.1109\/EEEIC.2018.8494576"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8783","DOI":"10.1109\/ACCESS.2020.2964678","article-title":"A study of computing zero crossing methods and an improved proposal for EMG signals","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"125331","DOI":"10.1109\/ACCESS.2020.3007873","article-title":"A novel method for measuring subtle alterations in pupil size in children with congenital strabismus","volume":"8","author":"Ibrahimi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Seguara, L., Zamora-Antu\u00f1ano, M., Resendiz, J., Paredes-Garc\u00eda, W., Altamirano-Corro, J., and Cruz-P\u00e9rez, M. (2020). Teaching challenges in COVID-19 scenery: Teams platform-based student satisfaction approach. Sustainability, 12.","DOI":"10.3390\/su12187514"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Barreda Luna, A.A., Kuri, G.H., Rodr\u00edguez-Res\u00e9ndiz, J., Zamora Antu\u00f1ano, M.A., Altamirano Corro, J.A., and Paredes-Garcia, W.J. (2022). Public space accessibility and machine learning tools for street vending spatial categorization. J. Maps, 1\u201310.","DOI":"10.1080\/17445647.2022.2035836"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106464","DOI":"10.1016\/j.knosys.2020.106464","article-title":"Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach","volume":"209","author":"Tobore","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gokul, H., Suresh, P., Hari Vignesh, B., Pravin Kumaar, R., and Vijayaraghavan, V. (September, January 24). Gait Recovery System for Parkinson\u2019s Disease using Machine Learning on Embedded Platforms. Proceedings of the 2020 IEEE International Systems Conference (SysCon), Montreal, QC, Canada.","DOI":"10.1109\/SysCon47679.2020.9275930"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"012074","DOI":"10.1088\/1742-6596\/1229\/1\/012074","article-title":"Atrial Fibrillation Detection Based on EEMD and XGBoost","volume":"1229","author":"Yue","year":"2019","journal-title":"J. Phys. Conf. 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