Related papers: How can AI reduce wrist injuries in the workplace?
Wearable devices like exoskeletons are designed to reduce excessive loads on specific joints of the body. Specifically, single- or two-degrees-of-freedom (DOF) upper-body industrial exoskeletons typically focus on compensating for the…
Using lower-limbs exoskeletons provides potential advantages in terms of productivity and safety associated with reduced stress. However, complex issues in human-robot interaction are still open, such as the physiological effects of…
Hand gesture recognition attracts great attention for interaction since it is intuitive and natural to perform. In this paper, we explore a novel method for interaction by using bone-conducted sound generated by finger movements while…
We propose a method for identifying individuals based on their continuously monitored wrist-worn accelerometry during activities of daily living. The method consists of three steps: (1) using Adaptive Empirical Pattern Transformation…
Classifying limb movements using brain activity is an important task in Brain-computer Interfaces (BCI) that has been successfully used in multiple application domains, ranging from human-computer interaction to medical and biomedical…
Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the noninvasive monitoring of…
In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable. Although existing photoplethysmography (PPG) heart rate sensors provide reliable measurements, they use considerably more energy than…
Goal: A limitation in robotic surgery is the lack of force feedback, due to challenges in suitable sensing techniques. To enhance the perception of the surgeons and precise force rendering, estimation of these forces along with tissue…
Ultrasound imaging has recently been introduced as a sensing interface for joint motion estimation. The use of ultrasound images as an estimation method is expected to improve the control performance of assistive devices and human--machine…
Continuous estimation of high-dimensional finger kinematics from forearm surface electromyography (EMG) could enable natural control for hand prostheses, AR/XR interfaces, and teleoperation. However, the complexity of human hand gestures…
Milling machines form an integral part of many industrial processing chains. As a consequence, several machine learning based approaches for tool wear detection have been proposed in recent years, yet these methods mostly deal with standard…
Designing generalizable control policies for lower-limb exoskeletons remains fundamentally constrained by exhaustive data collection or iterative optimization procedures, which limit accessibility to clinical populations. To address this…
Recent advancements in learning from human demonstration have shown promising results in addressing the scalability and high cost of data collection required to train robust visuomotor policies. However, existing approaches are often…
We propose MetaEMG, a meta-learning approach for fast adaptation in intent inferral on a robotic hand orthosis for stroke. One key challenge in machine learning for assistive and rehabilitative robotics with disabled-bodied subjects is the…
Brain computer interface based assistive technology are currently promoted for motor rehabilitation of the neuromuscular ailed individuals. Recent studies indicate a high potential of utilising electroencephalography (EEG) to extract motor…
Acuity assessments are vital in critical care settings to provide timely interventions and fair resource allocation. Traditional acuity scores rely on manual assessments and documentation of physiological states, which can be…
Artificial intelligence (AI) has made significant advances in recent years and opened up new possibilities in exploring applications in various fields such as biomedical, robotics, education, industry, etc. Among these fields, human hand…
The focus of this paper is dynamic gesture recognition in the context of the interaction between humans and machines. We propose a model consisting of two sub-networks, a transformer and an ordered-neuron long-short-term-memory (ON-LSTM)…
This work develops a smart mat for monitoring body positions. We use Velostat as a force sensor resistance (FSR) to construct a sensor matrix over the mat to receive the pressure distribution of the patient's body, and then upload the…
Building robotic prostheses requires a sensor-based interface designed to provide the robotic hand with the control required to perform hand gestures. Traditional Electromyography (EMG) based prosthetics and emerging alternatives often face…