Magnetized Micropillar-Enabled Wearable Sensors for Touchless and Intelligent Information Communication

The wearable sensors have recently attracted considerable attentions as communication interfaces through the information perception, decoding, and conveying process. However, it is still challenging to obtain a sensor that can convert detectable signals into multiple outputs for convenient, efficient, cryptic, and high-capacity information transmission. Herein, we present a capacitive sensor of magnetic field based on a tilted flexible micromagnet array (t-FMA) as the proposed interaction interface. With the bidirectional bending capability of t-FMA actuated by magnetic torque, the sensor can recognize both the magnitude and orientation of magnetic field in real time with non-overlapping capacitance signals. The optimized sensor exhibits the high sensitivity of over 1.3 T−1 and detection limit down to 1 mT with excellent durability. As a proof of concept, the sensor has been successfully demonstrated for convenient, efficient, and programmable interaction systems, e.g., touchless Morse code and Braille communication. The distinguishable recognition of the magnetic field orientation and magnitude further enables the sensor unit as a high-capacity transmitter for cryptic information interaction (e.g., encoded ID recognition) and multi-control instruction outputting. We believe that the proposed magnetic field sensor can open up a potential avenue for future applications including information communication, virtual reality device, and interactive robotics.

Vision-Guided Recognition Method for Working State of Robot Arm based on Machine Learning

To realize the capture of the target in any position within the working range of the robot arm, a vision-guided target recognition and positioning control method based on machine learning was proposed to improve the accuracy of the working state recognition. The system grabs four different contours of workpieces (triangle, pentagon, round, square and place in the designated area as the task, and by using the MATLAB to process the image information, and all the connected domains are marked bythe neighborhood areamarking algorithm. Later, the logarithmic coordinates-Fourier transform template matchingmethod is adopted to identify workpiece types and extract their centroid as the positioning reference coordinates.Combined with the 3-DOF robot arm, the standard D-H parametric method is usedto establishthe robot arm kinematics model, and through the inverse operation of the robot armand according to the position of the workpiece coordinates,the joint angle of each robot arm can be obtained. Later, it is sent to the lower microcontroller Arduino through a serial port, and then the control instruction is completed by the Arduino torealize the capture and placement of the workpiece and complete the work state recognition task.The experimental results show that the working state recognition system can meet the design requirements.

Mandibular Coronoid Process Hypertrophy: Diagnosis and 20-Year Follow-Up with CBCT, MRI and EMG Evaluations

Coronoid process hypertrophy (CPH) consists of an abnormal volumetric increment of the mandibular coronoid process; as this process grows gradually, the infratemporal space needed for the rotation and translation of the mandible is reduced, which results in a reduction of the range of mouth opening and lateral excursion, limiting mouth opening. The purpose of this case report was to describe a rare case of hypertrophy of coronoid processes with associated temporomandibular ankylosis, monitored for over 20 years. The patient was first visited when he had a facial trauma at the age of 4. Then he was followed through clinical, functional, instrumental, bi-dimensional and three-dimensional radiological evaluations up to the age of 24. Physical therapy was initiated at the age of 10 to improve the condition of the masticatory muscles, while at the age of 14, Transcutaneous Electrical Nerve Stimulations were performed to reduce muscle tension and, a bite plane was delivered to control the parafunctional activity of the jaw in the night and self-control instruction was provided for daytime habits. The adult patient has not accepted surgical intervention; thus, the future objective is to continue monitoring over the years to avoid a detrimental progression of the medical condition through physical and functional therapies while waiting for patient consent to surgery if needed.

Comparing the Minimum Celeration Line and the Beat Your Personal Best Goal-Setting Approaches During the Mathematical Practice of Students Diagnosed with Autism

This study compared two goal-setting approaches found in the Precision Teaching literature, namely the minimum celeration line and the beat your personal best during the mathematical practice of three male students diagnosed with autism, aged 8–9. An adapted alternating treatments design with a control condition was embedded in a concurrent multiple baseline across participants design. Each approach was randomly allocated to either the multiplication/division (×÷) table of 18 or 19, while no approach was allocated to the ×÷14 table that acted as a control. Instruction utilized number families and consisted of (a) untimed practice, (b) frequency-building, (c) performance criteria, (d) graphing, and (e) a token economy. Upon practice completion, an assessment of maintenance, endurance, stability, and application (MESA) was conducted. Participants improved with both conditions and maintained their performance well, while improvements with the control condition were weak. The beat your personal best approach was highlighted as slightly more effective in terms of average performance and more efficient in terms of timings needed to achieve criterion. No differences were identified in terms of learning rate (i.e., celeration) or performance on the MESA. More research is warranted to identify which goal-setting procedure is more appropriate for students in special education.

Foot thermometry with mHeath-based supplementation to prevent diabetic foot ulcers: A randomized controlled trial

Background: Three previous clinical trials have found that thermometry use reduced diabetic foot ulcers (DFUs) incidence four- to ten-fold among individuals with diabetes at high-risk of developing a DFU. However, these benefits depend on patient adherence to self-assessment. Therefore, novel approaches to improve self-management thermometry adherence are needed. Our objective was to compare incidence of DFUs in the thermometry plus mobile health (mHealth) reminders intervention arm vs. thermometry-only control arm. Methods: We conducted a randomized trial, enrolling adults with type 2 diabetes mellitus at risk of foot ulcers (risk groups 2 or 3) but without foot ulcers at the time of recruitment and allocating them to control (instruction to use a liquid crystal-based foot thermometer daily) or intervention (same instruction supplemented with text and voice messages with reminders to use the device and messages to promote foot care) groups and followed for 18 months. The primary outcome was time to occurrence of DFU. A process evaluation was also conducted. Results: A total of 172 patients (63% women, mean age 61 years) were enrolled; 86 to each study group. More patients enrolled in the intervention arm had a history of DFU (66% vs. 48%). Follow-up for the primary endpoint was complete for 158 of 172 participants (92%). DFU cumulative incidence was 24% (19 of 79) in the intervention arm and 11% (9 of 79) in the control arm. After adjusting for history of foot ulceration and study site, the Hazard Ratio (HR) for DFU was 1.44 (95% CI 0.65, 3.22). Adherence to ≥80% of daily temperature measurements was 87% (103 of 118) among the study participants who returned the logbook, with no difference between the intervention and control arms. Conclusions: This trial contributes to the evidence about the value of mHealth in preventing diabetes foot ulcers. Trial registration: ClinicalTrials.gov NCT02373592 (27/02/2015)

Foot thermometry with mHeath-based supplementation to prevent diabetic foot ulcers: A randomized controlled trial (Preprint)

BACKGROUND Thermometry monitoring has proven to reduce the occurrence of diabetic foot ulcers (DFU). mHealth may contribute to enhance adherence to this effective intervention. OBJECTIVE Our objective was to compare incidence of DFU in the thermometry plus mHealth reminders intervention arm vs. thermometry-only control arm. METHODS We conducted a randomized trial enrolling adults with type 2 diabetes mellitus with foot-at-risk (risk groups 2 or 3) without foot ulcers and allocating them to control (instruction to use a liquid crystal-based foot thermometer daily) or to intervention (same instruction supplemented with text and voice messages with reminders to use the device and messages to promote foot care) and followed for 18 months. The primary outcome was time to occurrence of DFU. A process evaluation was also conducted. RESULTS A total of 172 patients (63% women, mean age 61 years) were enrolled, 86 to each study group. More patients enrolled in the intervention arm had a history of DFU (66% vs. 48%). Follow-up for the primary endpoint was complete for 158 of 172 participants (92%). DFU cumulative incidence was 24% (19 of 79) in the intervention arm and 11% (9 of 79) in the control arm. After adjusting for history of foot ulceration and study site, the HR for DFU was 1.44 (95% CI 0.65, 3.22). Adherence to ≥80% of daily temperature measurements was 87% (103 of 118) among the study participants that returned the logbook, without difference between intervention and control arm. CONCLUSIONS This trial contributes to the evidence about the value of mHealth to prevent diabetes foot ulcers. CLINICALTRIAL Clinical trials, NCT02373592. Registered 27 February 2015, https://clinicaltrials.gov/ct2/show/NCT02373592

Applied On-Chip Machine Learning for Dynamic Resource Control in Multithreaded Processors

In this paper, we propose a machine learning algorithm to control instruction fetch bandwidth in a simultaneous multithreaded CPU. In a simultaneous multithreaded CPU, multiple threads occupy pools of hardware resources in the same clock cycle. Under some conditions, one or more threads may undergo a period of inefficiency, e.g., a cache miss, thereby inefficiently using shared resources and degrading the performance of other threads. If these inefficiencies can be identified at runtime, the offending thread can be temporarily blocked from fetching new instructions into the pipeline and given time to recover from its inefficiency, and prevent the shared system resources from being wasted on a stalled thread. In this paper, we propose a machine learning approach to determine when a thread should be blocked from fetching new instructions. The model is trained offline and the parameters embedded in a CPU, which can be queried with runtime statistics to determine if a thread is running inefficiently and should be temporarily blocked from fetching. We propose two models: a simple linear model and a higher-capacity neural network. We test each model in a simulation environment and show that system performance can increase by up to 19% on average with a feasible implementation of the proposed algorithm.