Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor.

BACKGROUND: Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. METHOD: We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. RESULTS: Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ([Formula: see text] = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. CONCLUSION: Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.

Wearable sensors during drawing tasks to measure the severity of essential tremor.

Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls. IMUs were placed at three points (dorsum of hand, posterior forearm, posterior upper arm) of each participant's dominant arm. Movement disorder neurologists who were blinded to clinical information scored ET patients on the Fahn-Tolosa-Marin rating scale (FTM) and conducted phenotyping according to the recent Consensus Statement on the Classification of Tremors. The ratio of power spectral density of acceleration data in 4-12 Hz to 0.5-4 Hz bands and the total duration of the action were inputs to a support vector machine that was trained to classify the ET subtype. Regression analysis was performed to determine the relationship of acceleration and temporal data with the FTM scores. The results show that the sensor located on the forearm had the best classification and regression results, with accuracy of 85.71% for binary classification of ET versus control. There was a moderate to good correlation (r2 = 0.561) between FTM and a combination of power spectral density ratio and task time. However, the system could not accurately differentiate ET phenotypes according to the Consensus classification scheme. Potential applications of machine-based assessment of ET using wearable sensors include clinical trials and remote monitoring of patients.

A novel method for utilizing RF information from IEEE 802.11 frames in Software Defined Networks.

Wireless security research using Radio Frequency (RF) data is complex and costly, often requiring expensive equipment and extensive offline processing. To make wireless research more accessible, we have integrated RF signal features with the existing IEEE 802.11 frame layer 2/3 data using the Software Defined Networking (SDN) paradigm. Combining low-cost RF hardware, a novel SDN processing method, and unique RF processing architecture has resulted in a framework that enables advanced wireless security and control research at a significantly lower cost. The method for enabling such functionality consist of the following stages:•During the demodulation process, extract Carrier Frequency Offset (CFO) and Pilot Sub-Carrier Offset from the Frequency Offset Correction section, in addition to the Vector Magnitude from the equalization section.•Append the new RF information to the wireless frame buffer and pass it to the SDR kernel driver module to further process before transferring them to a P4 application via a single API instantiation.•Compile a custom P4 application that combines the new RF features alongside higher OSI layer features to perform advanced networking, security, or control plane operations.

Teaching while surrounded by smartphones.

Smartphones have changed the way the people behave, their expectations, and interact with other people. Being more young people centric, these are now ubiquitous in the education environment which has higher representation of the youth, and have dramatically changed how our students behave in the classroom, what they expect, and how they learn. These devices offer both an opportunity and a threat to the education process and educators must take them into account when designing any education activity. We may wish them away, but that is getting less possible by the day. This paper investigates two scenarios, and these case studies highlight what can go right and wrong, and suggests processes that make it more likely that an educational process will be enhanced by the use of smart phones.

A three tier rapid mass programming method.

Various research activities require the programming of a large numbers of devices. This programming can be difficult to co-ordinate and organise, and requires considerable labour time. These issues often mean that testing on real hardware is abandoned or taken only to small scale implementation thus limiting the real-world findings. The method described in this paper adopts a three-tiered approach to programming large numbers of devices. Tier 1 is comprised of a single Master Controller which is networked to individual tower modules, these towers form the final 2 tiers with the Local Controller as tier 2 and up-to 15 target devices forming tier 3. The Master Controller co-ordinates and distributes the code for each device to the Local Controller which then programs the target devices. In the domain of networking this allows for: •Large networks of varied protocols to be programmed quickly, since towers are programmed in parallel, additional towers don't extend programming times.•Distributed networks are possible since towers are controlled over Ethernet.•Dramatically reduced labour time and defect rates due to human error in setting up devices.•This paper presents the implementation of this method for IoT Networking research with ESP-01 Target devices.

An unusual cause of pacemaker current leakage causing muscle stimulation.

Stimulation of skeletal muscle is a fairly common complication of permanent cardiac pacing. The usual cause is direct apposition of the anode of a unipolar pacemaker with muscle surrounding the pocket. Other causes include electrode insulation defects and electrode displacement. In this report we describe an unusual cause of muscle stimulation related to direct current leakage from the pacemaker.