{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T16:31:09Z","timestamp":1745253069042,"version":"3.37.3"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["2106996, 2050910"],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2022,12,21]]},"abstract":"Brain health attracts more recent attention as the population ages. Smartphone-based gait sensing and analysis can help identify the risks of brain diseases in daily life for prevention. Existing gait analysis approaches mainly hand-craft temporal gait features or developing CNN-based feature extractors, but they are either prone to lose some inconspicuous pathological information or are only dedicated to a single brain disease screening. We discover that the relationship between gait segments can be used as a principle and generic indicator to quantify multiple pathological patterns. In this paper, we propose NeuralGait, a pervasive smartphone-cloud system that passively captures and analyzes principle gait segments relationship for brain health assessment. On the smartphone end, inertial gait data are collected while putting the smartphone in the pants pocket. We then craft local temporal-frequent gait domain features and develop a self-attention-based gait segment relationship encoder. Afterward, the domain features and relation features are fed to a scalable RiskNet in the cloud for brain health assessment. We also design a pathological hot update protocol to efficiently add new brain diseases in the RiskNet. NeuralGait is practical as it provides brain health assessment with no burden in daily life. In the experiment, we recruit 988 healthy people and 417 patients with a single or combination of PD, TBI, and stroke, and evaluate the brain health assessment using a set of specifically designed metrics including global accuracy, exact accuracy, sensitivity, and false alarm rate. We also demonstrate the generalization (e.g., analysis of feature effectiveness and model efficiency) and inclusiveness of NeuralGait.<\/jats:p>","DOI":"10.1145\/3569476","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T20:34:01Z","timestamp":1673469241000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":46,"title":["NeuralGait"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3865-3587","authenticated-orcid":false,"given":"Huining","family":"Li","sequence":"first","affiliation":[{"name":"University at Buffalo, the State University of New York, Department of Computer Science and Engineering, Amherst, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3464-9441","authenticated-orcid":false,"given":"Huan","family":"Chen","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Electrical and Computer Engineering Department, Cleveland, OH, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1395-4570","authenticated-orcid":false,"given":"Chenhan","family":"Xu","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Department of Computer Science and Engineering, Amherst, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1880-5096","authenticated-orcid":false,"given":"Zhengxiong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Colorado Denver, Department of Computer Science and Engineering, Denver, CO, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-4208","authenticated-orcid":false,"given":"Hanbin","family":"Zhang","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Department of Computer Science and Engineering, Amherst, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7543-4696","authenticated-orcid":false,"given":"Xiaoye","family":"Qian","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Electrical and Computer Engineering Department, Cleveland, OH, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9140-2483","authenticated-orcid":false,"given":"Dongmei","family":"Li","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center, Rochester, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2269-4694","authenticated-orcid":false,"given":"Ming-chun","family":"Huang","sequence":"additional","affiliation":[{"name":"Duke Kunshan University, Suzhou, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6444-9411","authenticated-orcid":false,"given":"Wenyao","family":"Xu","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Department of Computer Science and Engineering, Amherst, NY, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"What is brain health and why is it important?\" BMJ","author":"Wang Y.","year":"2020","unstructured":"Y. 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Zhang \"Mobiledeeppill: A small-footprint mobile deep learning system for recognizing unconstrained pill images \" in Proceedings of the 15th Annual International Conference on Mobile Systems Applications and Services 2017 pp. 56--67."}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3569476","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T11:30:25Z","timestamp":1704972625000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3569476"}},"subtitle":["Assessing Brain Health Using Your Smartphone"],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":56,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12,21]]}},"alternative-id":["10.1145\/3569476"],"URL":"https:\/\/doi.org\/10.1145\/3569476","relation":{},"ISSN":["2474-9567"],"issn-type":[{"type":"electronic","value":"2474-9567"}],"subject":[],"published":{"date-parts":[[2022,12,21]]},"assertion":[{"value":"2023-01-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}