Egocentric vision (a.k.a. first-person vision–FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or parts of them?); interpretation (what are the hands doing?); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided.

We present the first public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames. Through extensive experiments with multiple facial landmark detection algorithms, including state-of-the-art convolutional neural network (CNN) models, we demonstrated the presence of bias in the landmark localization accuracy of pre-trained face alignment approaches in our participant groups. The pre-trained models produced higher errors in the two clinical groups compared to age-matched healthy control subjects. We also investigated how this bias changes when the existing models are fine-tuned using data from the target population. The release of this dataset aims to propel the development of face alignment algorithms robust to the presence of oro-facial impairment, support the automatic analysis and recognition of oro-facial gestures, enhance the automatic identification of neurological diseases, as well as the estimation of disease severity from videos and images.

Spinal cord injury (SCI) causes severe motor and sensory deficits, and there are currently no approved treatments for recovery. Nearly 70% of patients with SCI experience pathological muscle cocontraction and spasticity, accompanied by clinical signs such as patellar hyperreflexia and ankle clonus. The integration of epidural electrical stimulation (EES) of the spinal cord with rehabilitation has substantial potential to improve recovery of motor functions; however, abnormal muscle cocontraction and spasticity may limit the benefit of these interventions and hinder the effectiveness of EES in promoting functional movements. High-frequency excitation block introduced in peripheral nerve stimulation could reduce abnormal activity and lead to more physiological activation patterns. Here, we evaluated the application of high-frequency EES (HF-EES) in alleviating undesired muscular cocontraction and spasticity in two patients with motor incomplete SCI implanted with a commercial 32-channel EES paddle commonly used for pain therapy. To design custom HF-EES protocols, we first mapped the muscles targeted by different EES configurations. Our results showed that HF-EES substantially reduced patellar reflex in one participant and eliminated both patellar reflex and ankle clonus in the other participant. By combining HF-EES and low-frequency EES (LF-EES) to enhance functional movements with intensive rehabilitation, we observed notable improvements in lower limb kinematics, muscle strength, and clinical lower limb motor assessments over the trial period. This study suggests that HF-EES could be an important supplementary tool in SCI treatment, emphasizing the importance of personalized rehabilitation approaches and advanced tools to optimize EES treatments and offering hope for individuals with SCI-related motor deficits.