Falls are a major cause of injury among elderly adults. In addition, recent research indicates that elderly adults often fall because of the same individual biomechanical causes. Therefore, real-time individual fall risk assessment is crucial for designing a more effective and personalized fall reduction training program. However, existing methods for fall risk assessment either raise privacy concerns or require multiple wearable sensors. In this paper, we propose EgoFall, a real-time privacy-preserving fall risk assessment system using a commercial tracking camera. EgoFall utilizes a chest-mounted tracking camera and a carefully designed data pre-processing pipeline to acquire the ego-body motion data of the subject. The ego-body motion data is then fed to a lightweight CNN-Transformer model for fall risk assessment. We demonstrate that EgoFall not only outperforms the baseline methods but also has very low computational complexity, which is highly suitable for real-time processing.

We develop a decoding technique that estimates both the position and torque of a joint of the limb in interaction with an environment based on activities of the agonist-antagonist pair of muscles using electromyography in real time. The long short-term memory (LSTM) network is employed as the core processor of the proposed technique that is capable of learning time series of a long-time span with varying time lags. A validation that is conducted on the wrist joint shows that the decoding approach provides an agreement of greater than 95% in kinetics (i.e. torque) estimation and an agreement of greater than 85% in kinematics (i.e. angle) estimation, between the actual and estimated variables, during interactions with an environment. Also demonstrated is the fact that the proposed decoding method inherits the strengths of the LSTM network in terms of the capability of learning EMG signals and the corresponding responses with time dependency.

This study presents an impedance controller for series elastic actuators (SEAs), using the singular perturbation (SP) theory and time-delay estimation (TDE) technique. While the SP theory attenuates the requirement for states to be measured, the TDE technique eliminates the requirement for identifying system parameters. Through a numerical analysis and experimental validation, we demonstrate that the proposed controller produces satisfactory tracking performance while-at the same time-pursues wider operational bandwidth and lower driving-point impedance.