Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user’s forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user’s hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user’s gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.

Continuum robots have been the subject of extensive research due to their potential use in a wide range of applications. In this paper, we propose a new continuum robot with three backbones, and provide a unified analytic method for the kinematics and dynamics of a multiple-backbone continuum robot. The robot achieves actuation by independently pulling three backbones to carry out a bending motion of two-degreesof-freedom (DoF). A three-dimensional CAD model of the robot is built and the kinematical equation is established on the basis of the Euler-Bernoulli beam. The dynamical model of the continuum robot is constructed by using the Lagrange method. The simulation and the experiment's validation results show the continuum robot can exactly bend into pre-set angles in the two-dimensional space (the maximum error is less than 5% of the robot length) and can make a circular motion in three-dimensional space. The results demonstrate that the proposed analytic method for the kinematics and dynamics of a continuum robot is feasible.

Facial expressions are among behavioral signs of pain that can be employed as an entry point to develop an automatic human pain assessment tool. Such a tool can be an alternative to the self-report method and particularly serve patients who are unable to self-report like patients in the intensive care unit and minors. In this paper, a wearable device with a biosensing facial mask is proposed to monitor pain intensity of a patient by utilizing facial surface electromyogram (sEMG). The wearable device works as a wireless sensor node and is integrated into an Internet of Things (IoT) system for remote pain monitoring. In the sensor node, up to eight channels of sEMG can be each sampled at 1000 Hz, to cover its full frequency range, and transmitted to the cloud server via the gateway in real time. In addition, both low energy consumption and wearing comfort are considered throughout the wearable device design for long-term monitoring. To remotely illustrate real-time pain data to caregivers, a mobile web application is developed for real-time streaming of high-volume sEMG data, digital signal processing, interpreting, and visualization. The cloud platform in the system acts as a bridge between the sensor node and web browser, managing wireless communication between the server and the web application. In summary, this study proposes a scalable IoT system for real-time biopotential monitoring and a wearable solution for automatic pain assessment via facial expressions.

The NAD+-dependent deacetylase and mono-ADP-ribosyl transferase SIRT6 stabilizes the genome by promoting DNA double strand break repair, thereby acting as a tumor suppressor. However, whether SIRT6 regulates nucleotide excision repair (NER) remains unknown. Here, we showed that SIRT6 was recruited to sites of UV-induced DNA damage and stimulated the repair of UV-induced DNA damage. Mechanistic studies further indicated that SIRT6 interacted with DDB2, the major sensor initiating global genome NER (GG-NER), and that the interaction was enhanced upon UV irradiation. SIRT6 deacetylated DDB2 at two lysine residues, K35 and K77, upon UV stress and then promoted DDB2 ubiquitination and segregation from chromatin, thereby facilitating downstream signaling. In addition, we characterized several SIRT6 mutations derived from melanoma patients. These SIRT6 mutants ablated the stimulatory effect of SIRT6 on NER and destabilized the genome due to (i) partial loss of enzymatic activity (P27S or H50Y), (ii) a nonsense mutation (R150*) or (iii) high turnover rates (G134W). Overall, we demonstrate that SIRT6 promotes NER by deacetylating DDB2, thereby preventing the onset of melanomagenesis.