{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:50:45Z","timestamp":1760057445290,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Seoul Women\u2019s University","award":["2024-0006","QR240004","2022-0-00995"],"award-info":[{"award-number":["2024-0006","QR240004","2022-0-00995"]}]},{"name":"Seoul Business Agency(SBA) funded by Seoul Metropolitan Government","award":["2024-0006","QR240004","2022-0-00995"],"award-info":[{"award-number":["2024-0006","QR240004","2022-0-00995"]}]},{"name":"Korea government(MSIT)","award":["2024-0006","QR240004","2022-0-00995"],"award-info":[{"award-number":["2024-0006","QR240004","2022-0-00995"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent times, the emergence of Move-to-Earn (M2E) applications has revolutionized the intersection of digital innovation and physical wellness. Unlike their predecessors in the Play-to-Earn (P2E) domain, M2E apps incentivize physical activity, offering rewards for real-world movement such as walking or running. This shift aligns with a growing global focus on health consciousness that is propelled by the widespread adoption of smartphones and an increased awareness of the benefits of maintaining an active lifestyle. However, the rising popularity of these platforms has also brought about new problematic activities, with some users exploiting additional automated devices to simulate physical activity and claim rewards. In response, we propose an AI-based method aimed at distinguishing genuine user engagement from artificially generated auto-walker activity to ensure the integrity of reward distributions in M2E platforms. To demonstrate the generalizability of our model, we use a total of six open gait datasets and auto-walker datasets of automatic walking devices measured with various smartphones. Under unbiased and transparent evaluation, our model shows its ability to effectively discriminate auto-walker and genuine gait data not only on the seen datasets but also on the unseen datasets; it attained an F1-score of 0.997 on the auto-walker datasets and an F1-score of 1.000 on the genuine datasets.<\/jats:p>","DOI":"10.3390\/s25041002","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T11:03:51Z","timestamp":1738926231000},"page":"1002","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real Steps or Not: Auto-Walker Detection in Move-to-Earn Applications"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5216-0266","authenticated-orcid":false,"given":"Sunwoo","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Information Security, Seoul Women\u2019s University, 621, Hwarang-ro, Nowon-gu, Seoul 01797, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"ref_1","unstructured":"(2024, March 06). Move to Earn Fitness Apps Market Worth $1.8 Billion By 2030. Available online: https:\/\/www.grandviewresearch.com\/press-release\/global-move-to-earn-fitness-apps-market."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Raj, R., and Kos, A. (2023). An improved human activity recognition technique based on convolutional neural network. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-49739-1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"012087","DOI":"10.1088\/1742-6596\/1655\/1\/012087","article-title":"Random forest for human daily activity recognition","volume":"1655","author":"Nurwulan","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_4","unstructured":"Chathuramali, K.M., and Rodrigo, R. (2012, January 12\u201315). Faster human activity recognition with SVM. Proceedings of the International Conference on Advances in ICT for Emerging Regions (ICTer2012), Colombo, Sri Lanka."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mohsen, S., Elkaseer, A., and Scholz, S.G. (2021). Human Activity Recognition Using K-Nearest Neighbor Machine Learning Algorithm algorithm. Sustainable Design and Manufacturing. KES-SDM 2021. Smart Innovation, Systems and Technologies, Springer.","DOI":"10.1007\/978-981-16-6128-0_29"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1109\/TBME.2020.3048142","article-title":"Predicting multiple sclerosis from gait dynamics using an instrumented treadmill: A machine learning approach","volume":"68","author":"Kaur","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Takallou, M.A., Fallahtafti, F., Hassan, M., Al-Ramini, A., Qolomany, B., Pipinos, I., Myers, S., and Alsaleem, F. (2024). Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation. Sci. Rep., 14.","DOI":"10.1038\/s41598-023-50727-8"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s12984-016-0154-5","article-title":"Free-living gait characteristics in ageing and Parkinson\u2019s disease: Impact of environment and ambulatory bout length","volume":"13","author":"Godfrey","year":"2016","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cheng, D., Zou, Y., Xiang, S., and Jiang, C. (2025). Graph neural networks for financial fraud detection: A review. Front. Comput. Sci., 19.","DOI":"10.1007\/s11704-024-40474-y"},{"key":"ref_10","unstructured":"(2024, March 22). Charity Miles. Available online: https:\/\/charitymiles.org\/."},{"key":"ref_11","unstructured":"(2024, March 22). Sweatcoin. Available online: https:\/\/sweatco.in\/."},{"key":"ref_12","unstructured":"(2024, March 22). Evidation. Available online: https:\/\/evidation.com\/."},{"key":"ref_13","unstructured":"(2024, March 22). Betterpoints. Available online: https:\/\/betterpoints.app\/."},{"key":"ref_14","unstructured":"(2024, March 22). Runtopia. Available online: https:\/\/www.runtopia.net\/."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, S., Choi, W., and Lee, D.H. (2021, January 15\u201319). Usable user authentication on a smartwatch using vibration. Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea.","DOI":"10.1145\/3460120.3484553"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5447","DOI":"10.1109\/JIOT.2022.3221930","article-title":"From attack to identification: MEMS sensor fingerprinting using acoustic signals","volume":"10","author":"Lee","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103040","DOI":"10.1016\/j.cose.2022.103040","article-title":"The vibration knows who you are! A further analysis on usable authentication for smartwatch users","volume":"125","author":"Lee","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_22","first-page":"160","article-title":"Feature Engineering for Human Activity Recognition","volume":"12","author":"Atalaa","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl"},{"key":"ref_23","unstructured":"(2024, April 04). Smartphone and Smartwatch Inertial Measurements from Heterogeneous Subjects for Human Activity Recognition. Available online: https:\/\/zenodo.org\/records\/8398688."},{"key":"ref_24","unstructured":"(2024, March 04). Activities of Daily Living with Motion: A Dataset with Accelerometer, Magnetometer and Gyroscope Data from Mobile Devices. Available online: https:\/\/www.data-in-brief.com\/article\/S2352-3409(20)31508-0\/fulltext#seccesectitle0005."},{"key":"ref_25","unstructured":"(2020, August 29). Smartphones BQ Aquaris. Available online: https:\/\/www.bq.com\/pt\/smartphones."},{"key":"ref_26","unstructured":"(2024, April 04). FLAAP: An open Human Activity Recognition (HAR) Dataset for Learning and Finding the Associated Activity Patterns. Available online: https:\/\/data.mendeley.com\/datasets\/bdng756rgw\/1."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109809","DOI":"10.1016\/j.dib.2023.109809","article-title":"Dataset of inertial measurements of smartphones and smartwatches for human activity recognition","volume":"51","author":"Casteleyn","year":"2023","journal-title":"Data Brief"},{"key":"ref_28","unstructured":"(2024, April 04). MotionSense Dataset. Available online: https:\/\/github.com\/mmalekzadeh\/motion-sense."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Malekzadeh, M., Clegg, R.G., Cavallaro, A., and Haddadi, H. (2019, January 15\u201318). Mobile Sensor Data Anonymization. Proceedings of the International Conference on Internet of Things Design and Implementation, Montreal, QC, Canada.","DOI":"10.1145\/3302505.3310068"},{"key":"ref_30","unstructured":"(2024, April 04). Gait Database. Available online: https:\/\/figshare.com\/articles\/dataset\/Gait_Database\/20346852."},{"key":"ref_31","unstructured":"(2024, April 04). The USC-SIPI Human Activity Dataset. Available online: https:\/\/sipi.usc.edu\/had\/."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2012, January 5\u20138). USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370438"},{"key":"ref_33","unstructured":"(2024, April 04). MotionNode Is the Easy to Use Inertial Sensor Module. Available online: http:\/\/www.motionnode.com\/."},{"key":"ref_34","unstructured":"(2024, April 04). List of Automatic Walking Devices on AliExpress. Available online: https:\/\/www.aliexpress.com\/w\/wholesale-automatic-walking-swing-mobile-phone.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/4\/1002\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:29:03Z","timestamp":1760027343000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/4\/1002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,7]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["s25041002"],"URL":"https:\/\/doi.org\/10.3390\/s25041002","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,2,7]]}}}