###### Abstract  
Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has an imbalanced nature. Moreover, we designed a deep learning model that combines a convolution-based feature extractor and deep neural network blocks, the LSTM block, and the transformer encoder block, followed by a position-wise feedforward layer. We found that combining the input sequence with the convolution-learned features of different kernels tends to increase the performance of the fall-detection model. Last, we analyzed that the sensor signals collected by both accelerometer and gyroscope sensors can be leveraged to develop an effective classifier that can accurately detect falls, especially differentiating falls from near-falls. Furthermore, we also used data from sixteen different body parts and compared them to determine the better sensor position for fall-detection methods. We found that the shank is the optimal position for placing our sensors, with an F1 score of 0.97, and this could help other researchers collect high-quality fall datasets. fall detection; imbalanced dataset; sliding-window segmentation; wearable sensors; deep learning +
Footnote †: journal:  
detection offers a cheaper alternative to detect falls based on one or more wearable sensors that are attached to the user's body or clothing so they can be carried everywhere. The analysis of signals obtained from wearable sensors mounted on the human body is commonly used to monitor the health status of older patients with motion-assistive devices [7]. These sensors typically generate complex hip motion signals that are difficult to interpret without expert intervention. A computationally efficient fall-detection modeling technique is required to provide a meaningful characterization of the sensor data, which could be leveraged to trigger the pneumatic actuation of a proactive airbag. This automatically analyzes sensor readings to infer the kind of human activity performed by a user. Many researchers have been developing supervised-based fall-detection methods in the last decades [4; 5; 6]. However, there are still some limitations to obtaining fall datasets that contain near-fall activities, which are the cause of false positives and false negatives. The most common methods for fall-detection systems that use wearable sensors involve thresholding or machine learning techniques [4]. In the simple threshold fall-detection method, they calculate the threshold values of either the magnitude or the vertical projection of the acceleration [8; 9]. When either values are greater than the specified thresholds, a fall is detected. In a related work, [PERSON] uses both upper thresholds for the acceleration magnitude [10]. If the upper threshold is exceeded in less than 1 s, a fall template using a wavelet transform is used for comparison with the acceleration signal. A fall is detected if the comparison produces a high similarity value. Although automatic fall detection using a threshold-based method of individual parameters calculated from accelerometry measurements has a high sensitivity, it has a relatively low specificity [11]. For example, [PERSON] used a triaxial accelerometer to set the acceleration and torso orientation thresholds through experiments to detect falls, which achieved an accuracy of 81% [12].