A novel coupling quality index to estimate the coupling efficiency in Vibrant Soundbridge.

BACKGROUND: The objective-based methods for intraoperative monitoring have been suggested to assess the coupling and the outcomes of Vibrant Soundbridge (VSB). Although several techniques were proposed, they have not been widely adopted due to their complexity and invasiveness. PURPOSE: This study aimed to investigate the accuracy of a new coupling quality index using an intraoperative ABR threshold via AcoustiAP and its correlation with the perioperative measures. METHODS: This is a prospective study conducted at a tertiary center. The medical records were retrieved for all patients who underwent VSB implantation and had an intraoperative objective assessment for the coupling efficiency. AcoustiAP was used to evaluate the intraoperative ABR thresholds, which were assessed directly after the floating mass transducer (FMT) placement using acoustic CE-Chirp signals. The Vibrogram was used for the postoperative audiological evaluation. A new coupling quality index was calculated based on the intraoperative ABR thresholds. RESULTS: Ten patients were eligible for the present study. The ABR thresholds for good coupling ranged from 35 to 60 dBnHL. The loose coupling thresholds ranged considerably from 40 to 100 dBnHL. Overall, the median intraoperative ABR threshold at good coupling was 42.5 (40-60) dBnHL and 60 (40-100) dBnHL at loose coupling. The analysis showed that there was a significant change in the coupling quality index at the good and loose coupling points (24.3 ± 14 vs 38.8 ± 18.2, respectively, p < 0.001). At a cut-off value of 22.6 dB, the coupling quality index had a sensitivity of 70%  and specificity of 90% for discriminating good and loose coupling. CONCLUSION: This study provides evidence for the utility of intraoperative ABR measurements in predicting the coupling efficiency in patients with VSB. Our results showed that the coupling quality index had an acceptable accuracy in discriminating between good and poor coupling, which can help clinicians optimize the fitting process for individuals and may ultimately lead to improved patient outcomes.

Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device.

The side effects and complications of traditional treatments for treating pathological tremor have led to a growing research interest in wearable tremor suppression devices (WTSDs) as an alternative approach. Similar to how the human brain coordinates the function of the human system, a tremor estimator determines how a WTSD functions. Although many tremor estimation algorithms have been developed and validated, whether they can be implemented on a cost-effective embedded system has not been studied; furthermore, their effectiveness on tremor signals with multiple harmonics has not been investigated. Therefore, in this study, four tremor estimators were implemented, evaluated, and compared: Weighted-frequency Fourier Linear Combiner (WFLC), WFLC-based Kalman Filter (WFLC-KF), Band-limited Multiple FLC, and enhanced High-order WFLC-KF (eHWFLC-KF). This study aimed to evaluate the performance of each algorithm on a bench-top tremor suppression system with 18 recorded tremor motion datasets; and compare the performance of each estimator. The experimental evaluation showed that the eHWFLC-KF-based WTSD achieved the best performance when suppressing tremor with an average of 89.3% reduction in tremor power, and an average error when tracking voluntary motion of 6.6°/s. Statistical analysis indicated that the eHWFLC-KF-based WTSD is able to reduce the power of tremor better than the WFLC and WFLC-KF, and the BMFLC-based WTSD is better than the WFLC. The performance when tracking voluntary motion is similar among all systems. This study has proven the feasibility of implementing various tremor estimators in a cost-effective embedded system, and provided a real-time performance assessment of four tremor estimators.

Real-Time Voluntary Motion Prediction and Parkinson's Tremor Reduction Using Deep Neural Networks.

Wearable tremor suppression devices (WTSD) have been considered as a viable solution to manage parkinsonian tremor. WTSDs showed their ability to improve the quality of life of individuals suffering from parkinsonian tremor, by helping them to perform activities of daily living (ADL). Since parkinsonian tremor has been shown to be nonstationary, nonlinear, and stochastic in nature, the performance of the tremor models used by WTSDs is affected by their inability to adapt to the nonlinear behaviour of tremor. Another drawback that the models have is their limitation to estimate or predict one step ahead, which introduces delay when used in real time with WTSDs, which compromises performance. To address these issues, this work proposes a deep neural network model that learns the correlations and nonlinearities of tremor and voluntary motion, and is capable of multi-step prediction with minimal delay. A generalized model that is task and user-independent is presented. The model achieved an average estimation percentage accuracy of 99.2%. The average future voluntary motion prediction percentage accuracy with 10, 20, 50, and 100 steps ahead was 97.0%, 94.0%, 91.6%, and 89.9%, respectively, with prediction time as low as 1.5 ms for 100 steps ahead. The proposed model also achieved an average of 93.8% ± 1.5% in tremor reduction when it was tested in an experimental setup in real time. The tremor reduction showed an improvement of 25% over the Weighted Fourier Linear Combiner (WFLC), an estimator commonly used with WTSDs.

Design and Preliminary Performance Assessment of a Wearable Tremor Suppression Glove.

OBJECTIVE: Approximately 25% of individualsliving with parkinsonian tremor do not respond to traditional treatments. Wearable tremor suppression devices (WTSD) provide an alternative approach, however, tremor in the fingers has not been given as much attention as tremor in the elbow and the wrist. Therefore, the objective of this study is to design a wearable tremor suppression glove that can suppress tremor simultaneously, but independently, in multiple hand joints without restricting the user's voluntary motion. METHODS: A WTSD was designed for managing tremor in the index finger metacarpophalangeal (MCP) joint, thumb MCP joint, and the wrist. The prototype was tested and assessed on a participant living with parkinsonian tremor. RESULTS: The experimental evaluation showed an overall suppression of 73.1%, 80.7%, and 85.5% in resting tremor, 70.2%, 79.5%, and 81% in postural tremor, and 60.0%, 58.7%, and 65.0% in kinetic tremor in the index finger MCP joint, the thumb MCP joint, and the wrist, respectively. CONCLUSION: This first assessment of a WTSD for people living with Parkinson's disease provides confirmation of the feasibility of the approach. The next step requires a comprehensive validation on a broader population in order to evaluate the performance of the WTSD. SIGNIFICANCE: This study demonstrates the feasibility of using a WTSD to manage hand and finger tremor. The device enriches the field of upper-limb tremor management, as the first WTSD for multiple joints of the hand.

Country perspectives on improving technical assistance in the health sector.

Background: This paper presents learnings from the Re-Imagining Technical Assistance for Maternal, Neonatal, and Child Health and Health Systems Strengthening (RTA) project implemented in the Democratic Republic of the Congo and Nigeria from April 2018 to September 2020 by JSI Research & Training Institute, Inc. and Sonder Collective and managed by the Child Health Task Force. The first of RTA's two phases involved multiple design research activities, such as human-centered design and co-creation, while the second phase focused on secondary analysis of interviews and reports from the design research. This paper explores the limitations of current technical assistance (TA) approaches and maps opportunities to improve how TA is planned and delivered in the health sector. Methods: We analyzed project reports and 68 interviews with TA funders, providers, and consumers to explore in greater detail their perspectives on TA, its characteristics and drawbacks as well as opportunities for improvement. We used qualitative content analysis techniques for this study.   Results: The issues surrounding TA included the focus on donor-driven agendas over country priorities, poor accountability between and within TA actors, inadequate skill transfer from TA providers to government TA consumers, an emphasis on quick fixes and short-term thinking, and inadequate governance mechanisms to oversee and manage TA. Consequently, health systems do not achieve the highest levels of resilience and autonomy. Conclusions: Participants in project workshops and interviews called for a transformation in TA centered on a redistribution of power enabling governments to establish their health agendas in keeping with the issues that are of greatest importance to them, followed by collaboration with donors to develop TA interventions. Recommended improvements to the TA landscape in this paper include nine critical shifts, four domains of change, and 20 new guiding principles.

The Design of a Parkinson's Tremor Predictor and Estimator Using a Hybrid Convolutional-Multilayer Perceptron Neural Network.

Parkinson's Disease (PD) is considered to be the second most common age-related neuroegenerative disorder, and it is estimated that seven to ten million people worldwide have PD. One of the symptoms of PD is tremor, and studies have shown that wearable assistive devices have the potential to assist in suppressing it. However, despite the progress in the development of these devices, their performance is limited by the tremor estimators they use. Thus, a need for a tremor model that helps the wearable assistive devices to increase tremor suppression without impeding voluntary motion remains. In this work, a user-independent and task-independent tremor and voluntary motion detection method based on neural networks is proposed. Inertial measurement units (IMUs) were used to measure acceleration and angular velocity from participants with PD, these data were then used to train the neural network. The achieved estimation percentage accuracy of voluntary motion was 99.0%, and the future prediction percentage accuracy was 97.3%, 93.7%, 91.4% and 90.3% for 10 ms, 20 ms, 50 ms and 100 ms ahead, respectively. The root mean squared error (RMSE) achieved for tremor estimation was an average of 0.00087°/s on new unseen data, and the future prediction average RMSE across the different tasks achieved was 0.001°/s, 0.002°/s, 0.020°/s and 0.049°/s for 1 ms, 2 ms, 5 ms, and 10 ms ahead, respectively. Therefore, the proposed method shows promise for use in wearable suppression devices.

NTRU-Like Random Congruential Public-Key Cryptosystem for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are the core of the Internet of Things and require cryptographic protection. Cryptographic methods for WSN should be fast and consume low power as these networks rely on battery-powered devices and microcontrollers. NTRU, the fastest and secure public key cryptosystem, uses high degree, N, polynomials and is susceptible to the lattice basis reduction attack (LBRA). Congruential public key cryptosystem (CPKC), proposed by the NTRU authors, works on integers modulo q and is easily attackable by LBRA since it uses small numbers for the sake of the correct decryption. Herein, RCPKC, a random congruential public key cryptosystem working on degree N=0 polynomials modulo q, is proposed, such that the norm of a two-dimensional vector formed by its private key is greater than q. RCPKC works as NTRU, and it is a secure version of insecure CPKC. RCPKC specifies a range from which the random numbers shall be selected, and it provides correct decryption for valid users and incorrect decryption for an attacker using LBRA by Gaussian lattice reduction. RCPKC asymmetric encryption padding (RAEP), similar to its NTRU analog, NAEP, is IND-CCA2 secure. Due to the use of big numbers instead of high degree polynomials, RCPKC is about 27 times faster in encryption and decryption than NTRU. Furthermore, RCPKC is more than three times faster than the most effective known NTRU variant, BQTRU. Compared to NTRU, RCPKC reduces energy consumption at least thirty times, which allows increasing the life-time of unattended WSNs more than thirty times.

Design of User-Independent Hand Gesture Recognition Using Multilayer Perceptron Networks and Sensor Fusion Techniques.

According to the World Health Organization, stroke is the third leading cause of disability. A common consequence of stroke is hemiparesis, which leads to the impairment of one side of the body and affects the performance of activities of daily living. It has been proven that targeting the motor impairments as early as possible while using wearable mechatronic devices as a robot assisted therapy, and letting the patient be in control of the robotic system, can improve the rehabilitation outcomes. However, despite the increased progress on control methods for wearable mechatronic devices, a need for a more natural interface that allows for better control remains. In this work, a user-independent gesture classification method based on a sensor fusion technique using surface electromyography (EMG) and an inertial measurement unit (IMU) is presented. The Myo Armband was used to extract EMG and IMU data from healthy subjects. Participants were asked to perform 10 types of gestures in 4 different arm positions while using the Myo on their dominant limb. Data obtained from 14 participants were used to classify the gestures using a Multilayer Perceptron Network. Finally, the classification algorithm was tested on 5 novel users, obtaining an average accuracy of 78.94%. These results demonstrate that by using the proposed approach, it is possible to achieve a more natural human machine interface that allows better control of wearable mechatronic devices during robot assisted therapies.

A simple safe, reliable and reproducible mechanism for producing experimental bite marks.

With improving technology it should be possible to develop an objective, reliable and valid method that can be undertaken by most forensic Odontologists without recourse to expensive or bulky equipment. One of the main factors that affect the physical appearance of bitemark is the amount of force applied during biting. There is little evidence relating the appearance of a bite mark to the amount of force applied and how that force relates to the biters maximal biteforce. This paper describes simple apparatus that can be used to inflict experimental bites on living subjects reproducibly and with minimal risk. The aims of this study are to report on the development of a mechanical apparatus that produces experimental bitemarks on living human subjects with a known force in a safe, reliable and reproducible manner and to relate the force applied during production of the experimental bitemark to the maximum bite force of the biter. Maximum bite force of one of the authors was determined as 324 N. Experimental bitemarks were inflicted on living subjects with known weights. Weights of up to 10 kg were well tolerated by the subjects. The relation between forces used to inflict bites and the maximum bite force of the author is reported, with 10 kg being approximately one third of the maximum bite force. The apparatus was well tolerated and the results were reliable and reproducible. The results from this study could help in determining the severity of bitemarks. This apparatus could help researchers in developing objective based bitemark analysis techniques.