Predicting fall risk is necessary for rescue and accident prevention in the elderly. In this study, deep learning regression models were used to predict the acceleration sum vector magnitude (SVM) peak value, which represents the risk of a fall. Twenty healthy adults (aged 22.0±1.9 years, height 164.9±5.9 cm, weight 61.4±17.1 kg) provided data for 14 common daily life activities (ADL) and 11 falls using IMU (Inertial Measurement Unit) sensors (Movella Dot, Netherlands) at the S2. The input data includes information from 0.7 to 0.2 seconds before the acceleration SVM peak, encompassing 6-axis IMU data, as well as acceleration SVM and angular velocity SVM, resulting in a total of 8 feature vectors used to model training. Data augmentations were applied to solve data imbalances. The data was split into a 4 : 1 ratio for training and testing. The models were trained using Mean Squared Error (MSE) and Mean Absolute Error (MAE). The deep learning model utilized 1D-CNN and LSTM. The model with data augmentation exhibited lower error values in both MAE (1.19 g) and MSE (2.93g²). Low-height falls showed lower predicted acceleration peak values, while ADLs like jumping and sitting showed higher predicted values, indicating higher risks.

Free fall safety brakes against accidental cable failure such as in elevators may require friction, wedging action, eddy current, and other effects. An ideal safety brake system should be quick in its deployment with sufficient payload capacity in compact dimensions. In this study, a safety braking system with a quick deployment mechanism is proposed. The mechanism housed in a carrier is suspended by the cable and connected to the payload. At the onset of cable failure, a linkage system is driven by a pre-loaded spring to drive terminal cutting tools tips against the sacrificial braking pads on each side of the vertical track. Experiments showed that large braking force may be achieved by a compact mechanism. Several design issues of linkage deployment, braking force control, and drop dynamics are discussed.