Falls are the second leading cause of unintentional injury deaths worldwide. While numerous wearable fall detection devices incorporating AI models have been developed, none of them are used successfully in a fall detection application running on commodity-based smartwatches in real time. The system misses some falls, and generates an annoying amount of False Positives for practical use. We have investigated and experimented with an LSTM model for fall detection on a smartwatch. Even though the LSTM model has high accuracy during offline testing, the good performance of offline LSTM models cannot be translated to the equivalence of real-time performance. Transformers, on the other hand, can learn long-sequence data and patterns intrinsic to the data due to their self-attention mechanism. This paper compares three variants of LSTM and two variants of Transformer models for learning fall patterns. We trained all models using fall and activity data from three datasets, and the real-time testing of the model was performed using the SmartFall App. Our findings showed that in the offline training, the CNN-LSTM model was better than the Transformer model for all the datasets. However, the Transformer is a preferable choice for deployment in real-time fall detection applications.

Android application programming interface (API) enables app developers to harness the functionalities of Android devices by interfacing with services and hardware using a Software Development Kit (SDK). However, API frequently evolves together with its associated SDK, and compatibility issues may arise when the API level supported by the underlying device differs from the API level targeted by app developers. These issues can lead to unexpected behaviors, resulting in a bad user experience. This article presents ACID, a novel approach to detecting Android API compatibility issues induced by API evolution. It detects both API invocation compatibility issues and API callback compatibility issues using API differences and static analysis of the app code. Experiments with 20 benchmark apps show that ACID is more accurate and faster than the state-of-the-art techniques in detecting API compatibility issues. The application of ACID on 2965 real-world apps further demonstrates its practical applicability. To eliminate the false positives reported by ACID, this article also presents a simple yet effective method to quickly verify the compatibility issues by selecting and executing the relevant tests from app's test suite, and experimental results demonstrate the verification method can eliminate most false positives when app's test suite has good coverage of the API usages.

Within the hazardous construction industry, roofers represent one of the most at-risk workforces, with high fatalities and injury rates largely driven by Work-Related Musculoskeletal Disorders (WMSDs). The primary precursor to these disorders is muscle fatigue, yet its objective assessment remains a significant challenge for implementing proactive safety management. To address this gap, this study details the implementation and validation of an AI-driven predictive analytics framework for automated fatigue detection using surface electromyography (sEMG) signals. Data was collected as participants (novice roofers) performed strenuous, simulated roofing tasks involving sustained standing, stooping, and kneeling postures. A key innovation is a data-driven labeling methodology using Weak Monotonicity (WM) trend analysis to automate the generation of objective labels. After a feature selection process yielded seven significant features, an evaluation of standard models confirmed that their classification performance was highly posture-dependent, motivating a more robust, hybrid solution. The framework culminates in a high-performance hybrid machine learning model. This architecture synergistically combines a Transformer–LSTM network for deep feature extraction with an XGBoost classifier. The model outperformed all standalone approaches, achieving over 82% accuracy across all postures with consistently strong fatigue F1-scores (0.77–0.78). The entire framework was validated using a stringent Leave-One-Subject-Out (LOSO) cross-validation protocol to ensure subject-independent generalizability. This research provides a validated component for AI-enhanced safety management systems. Future work should prioritize field validation with professional workers to translate this framework into practical, real-world ergonomic monitoring systems.

The widespread adoption of smartphones and smartwatches has enabled non-intrusive fall detection through built-in sensors and on-device computation. While these devices are widely used by older adults, existing systems still struggle to accurately detect soft falls in real-world settings. There is a notable drop in performance when fall-detection models trained offline on labeled accelerometer data are deployed and tested in real-world conditions using streaming, real-time data. To address this, our experimental study investigates whether incorporating additional sensor modalities, specifically gyroscope data with accelerometer data from wrist and hip locations, can help bridge this performance gap. Through systematic experimentation, we demonstrated that combining accelerometer data from the hip and the wrist yields a model capable of achieving an F1-score of 88% using a Transformer-based neural network in offline evaluation, which is an improvement of 8% over a model trained solely on wrist accelerometer data. However, when it is deployed in an uncontrolled home environment with streaming real-time data, this model produced a high number of false positives. To address this, we retrained the model using feedback data that comprised both false positives and true positives and was collected from ten participants during real-time testing. This refinement yielded an F1-sore of 92% and significantly reduced false positives while maintaining comparable accuracy in detecting true falls in real-world settings. Furthermore, we demonstrated that the improved model generalizes well to older adults’ movement patterns, with minimal false-positive detections.

We present an edge-cloud collaborative personalized fall detection pipeline called P-Fall. A personalized fall detection model requires real-time adaptation of a pre-trained model using real-time feedback data provided by the user. We, herein, highlight the design of the software architecture for a collaborative framework, the smart-watch's UI for the ease of collecting a user's feedback data, and the automation of the personalization process.