How can AI reduce fall injuries in the workplace?

Fall-caused injuries are common in all types of work environments, including offices. They are the main cause of absences longer than three days, especially for small and medium-sized businesses (SMEs). However, data, data amount, data heterogeneity, and stringent processing time constraints continue to pose challenges to real-time fall detection. This work proposes a new approach based on a recurrent neural network (RNN) for Fall Detection and a Kolmogorov-Arnold Network (KAN) to estimate the time of impact of the fall. The approach is tested on SisFall, a dataset consisting of 2706 Activities of Daily Living (ADLs) and 1798 falls recorded by three sensors. The results show that the proposed approach achieves an average TPR of 82.6% and TNR of 98.4% for fall sequences and 94.4% in ADL. Besides, the Root Mean Squared Error of the estimated time of impact is approximately 160ms.

Recognition of Physiological Patterns during Activities of Daily Living Using Wearable Biosignal Sensors

A key aspect of developing fall prevention systems is the early prediction of a fall before it occurs. This paper presents a statistical overview of results obtained by analyzing 22 activities of daily living to recognize physiological patterns and estimate the risk of an imminent fall. The results demonstrate distinctive patterns between high-intensity and low-intensity activity using EMG, ECG, and respiration sensors, also indicating the presence of a proportional trend between movement velocity and muscle activity. These outcomes highlight the potential benefits of using these sensors in the future to direct the development of an activity recognition and risk prediction framework for physiological phenomena that can cause fall injuries.