Human Daily Activity and Fall Recognition Using a Smartphone’s Acceleration Sensor
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In some cases, Mobiact is used for pre-training because it has the largest number of participants.
“In some cases, Mobiact [5] is used for pre-training instead as it is has the largest number of participants.”
From Assessing the State of Self-Supervised Human Activity Recognition Using Wearables (2022) · cites this paper as [5]
For self-supervised HAR methods, model pre-training using unlabeled training data is typically performed either on the same dataset as the target activity recognition system without using label information, or on a larger background dataset such as Mobiact.
“For these methods, model pre-training using unlabeled training data is typically either performed utilizing the same dataset as used for training the target activity recognition system-but without using label information-or with a larger background dataset such as Mobiact [5].”
From Assessing the State of Self-Supervised Human Activity Recognition Using Wearables (2022) · cites this paper as [5]
The three public datasets include diverse participants, activities, and sensor configurations, providing standardized benchmarks for evaluating user-level generalization and ensuring fair comparison across studies.
“These datasets include diverse participants, activities, and sensor configurations, providing standardized benchmarks for evaluating user-level generalization and ensuring fair comparison across studies.”
From Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach (2025) · cites this paper as [39]
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References 29 · Cited by 91
- Authors
- Chatzaki, C., Pediaditis, M., Vavoulas, G., & Tsiknakis, M.
- Affiliations
- Hellenic Mediterranean University (GR)
- ORCID
- Tsiknakis, Manolis
- Venue
- Communications in Computer and Information Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 100-118
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- OpenAlex
- W2738611661
- Cited by
- 91
- References
- 29
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 18 · 2023: 19 · 2022: 9 · 2021: 15 · 2020: 7 · 2019: 7 · 2018: 2
- FWCI
- 1.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Communications in computer and information science (publishedVersion, public-domain)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, fall detection, activities of daily living, feature selection, classification, wearable sensing, benchmark dataset
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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