Federated Learning for Hierarchical Fall Detection and Human Activity Recognition
Bibliographic record
Abstract
In healthcare monitoring, precise fall detection (FD) and human activity recognition (HAR) are paramount, especially for the elderly. This paper presents a federated learning (FL) framework that employs a two-stage hierarchical approach to address these needs. The first stage distinguishes between fall and non-fall events, crucial for minimizing false alarms in sensitive environments such as elderly care facilities. Subsequently, if a fall is detected, the system classifies the type of fall to facilitate appropriate medical responses; if no fall is detected, it classifies the specific activity being performed. This approach enables accurate emergency responses and supports personalized healthcare interventions. With FL, all model training are conducted on local devices using wearable data, including inertial measurement unit (IMU) and physiological signals, without the need to share sensitive data centrally, preserving users’ privacy. This method ensures that each device contributes to a global model, whilst maintaining the confidentiality of individual data inputs. Our experimental evaluations demonstrate generalization capabilities in binary classification for FD and highlight challenges in multi-class scenarios for HAR, demonstrating the need for advanced strategies in handling complex classifications. The source code and experimental evaluations are accessible at https://github.com/SIT-FL/FL-Fall-Detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".