Using Portable Transducers to Measure Tremor Severity

Background: Portable motion transducers, suitable for measuring tremor, are now available at a reasonable cost. The use of these transducers requires knowledge of their limitations and data analysis. The purpose of this review is to provide a practical overview and example software for using portable motion transducers in the quantification of tremor. Methods: Medline was searched via PubMed.gov in December 2015 using the Boolean expression “tremor AND (accelerometer OR accelerometry OR gyroscope OR inertial measurement unit OR digitizing tablet OR transducer).” Abstracts of 419 papers dating back to 1964 were reviewed for relevant portable transducers and methods of tremor analysis, and 105 papers written in English were reviewed in detail. Results: Accelerometers, gyroscopes, and digitizing tablets are used most commonly, but few are sold for the purpose of measuring tremor. Consequently, most software for tremor analysis is developed by the user. Wearable transducers are capable of recording tremor continuously, in the absence of a clinician. Tremor amplitude, frequency, and occurrence (percentage of time with tremor) can be computed. Tremor amplitude and occurrence correlate strongly with clinical ratings of tremor severity. Discussion: Transducers provide measurements of tremor amplitude that are objective, precise, and valid, but the precision and accuracy of transducers are mitigated by natural variability in tremor amplitude. This variability is so great that the minimum detectable change in amplitude, exceeding random variability, is comparable for scales and transducers. Research is needed to determine the feasibility of detecting smaller change using averaged data from continuous long-term recordings with wearable transducers

A Computer Model of Intracranial Pressure Dynamics During Traumatic Brain Injury that Explicitly Models Fluid Flows and Volumes

This report documents a computer model of intracranial pressure (ICP) dynamics that is used to evaluate clinical treatment options for elevated ICP during traumatic brain injury (TBI). The model uses fluid volumes as primary state variables and explicitly models fluid flows as well as the resistance, compliance, and pressure associated with each of the compartments (arteries and arterioles, capillary bed, veins, venous sinus, ventricles, and brain parenchyma). The model has been tested to assure that it reproduces a correct physiologic response to intra-and extra-parenchymal hemorrhage and edema, and to therapies directed at reducing ICP such as cerebral spinal fluid drainage, mannitol administration, head elevation, and mild hyperventilation. The model is able to replicate observed clinical behavior in many cases, including elevated ICP associated with severe cerebral edema, subdural hematoma, and cerebrospinal fluid blockage. The model also successfully reproduces tne cerebrovascular regulatory mechanisms that are activated during TBI in response to various abnormalities such as high or low systemic blood pressure. We conclude that incorporating fluid volumes and flows into a model of lCP dynamics significantly improved its clinical utility. Additional improvements are anticipated (or wil1 accrue or will result) as the specific mechanisms that modify cerebral compliance and autoregulation during TBI and elevated ICP are further delineated

A novel particle filtering method for estimation of pulse pressure variation during spontaneous breathing

Background: We describe the first automatic algorithm designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPVPPV accurately and reliably in mechanically ventilated subjects, at the moment there is no automatic algorithm for estimating PPVPPV on spontaneously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). We report the performance assessment results of the proposed algorithm on real ABP signals from spontaneously breathing subjects. Results: Our assessment results indicate good agreement between the automatically estimated PPVPPV and the gold standard PPVPPV obtained with manual annotations. All of the automatically estimated PPVPPV index measurements (PPVautoPPVauto) were in agreement with manual gold standard measurements (PPVmanuPPVmanu) within ±4 % accuracy. Conclusion: The proposed automatic algorithm is able to give reliable estimations of PPVPPV given ABP signals alone during spontaneous breathing

Gait and Turning Characteristics from Daily Life increase ability to predict future falls in people with Parkinson’s disease

Objectives: To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson’s disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. Methods: We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a “best subsets selection strategy” was used to find combinations of measures that discriminated future fallers from non- fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls. Results: Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50–1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84–1.00]. From the top 10 models (all AUCs\u3e0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10). Conclusions: These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several dierent aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD

CONTINUOUS MONITORING OF MOVEMENT IN PATIENTS WITH PARKINSON'S DISEASE USING INERTIAL SENSORS

Gait impairment is a hallmark of Parkinson's disease (PD). The assessment of gait and balance in the clinic may not adequately reflect mobility in daily life. It is often reported that patients with PD walk better when they are examined in an outpatient clinic or in a research laboratory than at home. Continuous monitoring of mobility during spontaneous daily activities may provide clinicians and patients with objective measures of the quality of their mobility. We show that continuous monitoring of spontaneous gait with wearable inertial sensors during daily activities is feasible for patients with PD. We tested 13 patients with PD and 8 healthy controls to evaluate the feasibility of using wearable inertial sensors at home for one week. The inertial system successfully detects walking bouts and provides sixteen objective measures that can characterize gait changes in patients with PD

Electrical Skin Impedance at Acupuncture Points

Objective: To test whether electrical skin impedance at each of three acupuncture points (APs) is significantly lower than at nearby sites on the meridian (MP) and off the meridian (NP). Design: Two instruments—Prognos (MedPrevent GmbH, Waldershof, Germany), a constant-current (DC) device, and PT Probe (designed for this study), a 100-Hz sinusoidal-current (AC) device—were used to record electrical impedance at three APs (right Gallbladder 14, right Pericardium 8, and left Triple Energizer 1), and two control sites for each AP. Each AP, MP, and NP was measured four times in random order with each device. Setting: The study was conducted over a period of 4 days at the Oregon College of Oriental Medicine (OCOM). Subjects: Twenty (20) healthy adults (14 women and 6 men), all recruited from the OCOM student body and faculty, participated in the study. Results: The Prognos measurements had an intraclass correlation (ICC) 0.84 and coefficient of variation (CV) 0.43. The PT Probe had ICC 0.81 and CV 0.31. Impedance values at APs were not significantly less than at MPs or NPs. Impedance values at MPs were also not significantly less than NPs, although their individual p values were 0.05 in 4 of 6 cases. There was a significant trend of increasing impedance with repeated measurements with both the Prognos (p 0.003) and the PT Probe (p 0.003). Conclusions: Within the reliability limits of our study methods, none of the three APs tested has lower skin impedance than at either of the nearby control points. These results are not consistent with previous studies that detected lower skin impedance at APs than nearby sites. Further study is necessary to determine whether MPs have lower skin impedance than nearby NPs. Our study suggests caution is warranted when developing, using, and interpreting results from electrodermal screening devices. Further studies are needed to clarify the clinically important and controversial hypothesis that APs are sites of lower impedance

Inertial and Time-of-Arrival Ranging Sensor Fusion

Wearable devices with embedded kinematic sensors including triaxial accelerometers, gyroscopes, and magnetometers are becoming widely used in applications for tracking human movement in domains that include sports, motion gaming, medicine, and wellness. The kinematic sensors can be used to estimate orientation, but can only estimate changes in position over short periods of time. We developed a prototype sensor that includes ultra wideband ranging sensors and kinematic sensors to determine the feasibility of fusing the two sensor technologies to estimate both orientation and position. We used a state space model and applied the unscented Kalman filter to fuse the sensor information. Our results demonstrate that it is possible to estimate orientation and position with less error than is possible with either sensor technology alone. In our experiment we obtained a position root mean square error of 5.2 cm and orientation error of 4.8° over a 15 min recording

Opal Actigraphy (Activity and Sleep) Measures Compared to ActiGraph: A Validation Study

Physical activity and sleep monitoring in daily life provide vital information to track health status and physical fitness. The aim of this study was to establish concurrent validity for the new Opal Actigraphy solution in relation to the widely used ActiGraph GT9X for measuring physical activity from accelerometry epic counts (sedentary to vigorous levels) and sleep periods in daily life. Twenty participants (age 56 + 22 years) wore two wearable devices on each wrist for 7 days and nights, recording 3-D accelerations at 30 Hz. Bland–Altman plots and intraclass correlation coefficients (ICCs) assessed validity (agreement) and test–retest reliability between ActiGraph and Opal Actigraphy sleep durations and activity levels, as well as between the two different versions of the ActiGraph. ICCs showed excellent reliability for physical activity measures and moderate-to-excellent reliability for sleep measures between Opal versus Actigraph GT9X and between GT3X versus GT9X. Bland–Altman plots and mean absolute percentage error (MAPE) also show a comparable performance (within 10%) between Opal and ActiGraph and between the two ActiGraph monitors across activity and sleep measures. In conclusion, physical activity and sleep measures using Opal Actigraphy demonstrate performance comparable to that of ActiGraph, supporting concurrent validation. Opal Actigraphy can be used to quantify activity and monitor sleep patterns in research and clinical studies