Estimation of respiration rate and sleeping position using a wearable accelerometer

The 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBS Virtual Academy, 20-24 July 2020Wearable inertial sensors offer the possibility to monitor sleeping position and respiration rate during sleep, enabling a comfortable and low-cost method to remotely monitor patients. Novel methods to estimate respiration rate and position during sleep using accelerometer data are presented, with algorithm performance examined for two sensor locations, and accelerometer-derived respiration rate compared across sleeping positions. Eleven participants (9 male; aged: 47.82±14.14 years; BMI 30.9±5.27 kg/m 2 ; AHI 5.77±4.18) undergoing a scheduled clinical polysomnography (PSG) wore a tri-axial accelerometer on their chest and upper abdomen. PSG cannula flow and position data were used as benchmark data for respiration rate (breaths per minute, bpm) and position. Sleeping position was classified using logistic regression, with features derived from filtered acceleration and orientation. Accelerometer-derived respiration rate was estimated for 30 s epochs using an adaptive peak detection algorithm which combined filtered acceleration and orientation data to identify individual breaths. Sensor-derived and PSG respiration rates were then compared. Mean absolute error (MAE) in respiration rate did not vary between sensor locations (abdomen: 1.67±0.37 bpm; chest: 1.89±0.53 bpm; p=0.52), while reduced MAE was observed when participants lay on their side (1.58±0.54 bpm) compared to supine (2.43±0.95 bpm), p<; 0.01. MAE was less than 2 bpm for 83.6% of all 30 s windows across all subjects. The position classifier distinguished supine and left/right with a ROC AUC of 0.87, and between left and right with a ROC AUC of 0.94. The proposed methods may enable a low-cost solution for in-home, long term sleeping posture and respiration monitoring.European Research CouncilScience Foundation IrelandInsight Research Centre2020-10-06 JG: PDF replaced with correct versio

Comparison of acoustic voice features derived from mobile devices and studio microphone recordings

Objectives/Hypothesis Improvements in mobile device technology offer new opportunities for remote monitoring of voice for home and clinical assessment. However, there is a need to establish equivalence between features derived from signals recorded from mobile devices and gold standard microphone-preamplifiers. In this study acoustic voice features from android smartphone, tablet, and microphone-preamplifier recordings were compared. Methods Data were recorded from 37 volunteers (20 female) with no history of speech disorder and six volunteers with Huntington's disease (HD) during sustained vowel (SV) phonation, reading passage (RP), and five syllable repetition (SR) tasks. The following features were estimated: fundamental frequency median and standard deviation (F0 and SD F0), harmonics-to-noise ratio (HNR), local jitter, relative average perturbation of jitter (RAP), five-point period perturbation quotient (PPQ5), difference of differences of amplitude and periods (DDA and DDP), shimmer, and amplitude perturbation quotients (APQ3, APQ5, and APQ11). Results Bland-Altman analysis revealed good agreement between microphone and mobile devices for fundamental frequency, jitter, RAP, PPQ5, and DDP during all tasks and a bias for HNR, shimmer and its variants (APQ3, APQ5, APQ11, and DDA). Significant differences were observed between devices for HNR, shimmer, and its variants for all tasks. High correlation was observed between devices for all features, except SD F0 for RP. Similar results were observed in the HD group for SV and SR task. Biological sex had a significant effect on F0 and HNR during all tests, and for jitter, RAP, PPQ5, DDP, and shimmer for RP and SR. No significant effect of age was observed. Conclusions Mobile devices provided good agreement with state of the art, high-quality microphones during structured speech tasks for features derived from frequency components of the audio recordings. Caution should be taken when estimating HNR, shimmer and its variants from recordings made with mobile devices

Taking balance measurement out of the laboratory and into the home: discriminatory capability of novel centre of pressure measurement in fallers and non-fallers

We investigated three methods for estimating centre of pressure excursions, as measured using a portable pressure sensor matrix, in order to deploy similar technology into the homes of older adults for longitudinal monitoring of postural control and falls risk. We explored the utility of these three methods as markers of falls risk in a cohort of 120 community dwelling older adults with and without a history of falls (65 fallers, 55 non-fallers). A number of standard quantitative balance parameters were derived using each centre of pressure estimation method. Rank sum tests were used to test for significant differences between fallers and non-fallers while intra-class correlation coefficients were also calculated to determine the reliability of each method. A method based on estimating the changes in the magnitude of pressure exerted on the pressure sensor matrix was found to be the most reliable and discriminative. Our future work will implement this method for home-based balance measurement

Regression-based analysis of front crawl swimming using upper-arm mounted accelerometers

The 41st International Engineering in Medicine and Biology Conference, Berlin, Germany, 23-27 July 2019Wearable accelerometers can be used to quantify movement during swimming, enabling objective performance analysis. This study examined arm acceleration during front crawl swimming, and investigated how accelerometer-derived features change with lap times. Thirteen participants swam eight 50m laps using front crawl with a tri-axial accelerometer attached to each upper arm. Data were segmented into individual laps; lap times estimated and individual strokes extracted. Stroke times, root mean squared (RMS) acceleration, RMS jerk and spectral edge frequencies (SEF) were calculated for each stroke. Movement symmetry was assessed as the ratio of the minimum to maximum feature value for left and right arms. A regularized multivariate regression model was developed to estimate lap time using a subset of the accelerometer-derived features. Mean lap time was 56.99±11.99s. Fifteen of the 42 derived features were significantly correlated with lap time. The regression model included 5 features (stroke count, mean SEF of the X and Z axes, stroke count symmetry, and the coefficient of variation of stroke time symmetry) and estimated 50m lap time with a correlation coefficient of 0.86, and a cross-validated RMS error of 6.38s. The accelerometer-derived features and developed regression model may provide a useful tool to quantitatively evaluate swimming performance.European Research CouncilScience Foundation IrelandInsight Research Centre2020-02-13 JG: docx replaced with INSIGHT submitted PD

Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test.

We assess the relative performance of a suite of selected models to interpret three-phase oil relative permeability data and provide a procedure to determine identifiability of the model parameters. We ground our analysis on observations of Steady-State two-and three-phase relative permeabilities we collect on a water-wet Sand-Pack sample through series of core-flooding experiments. Three-phase experiments are characterized by simultaneous injection of water and gas into the core sample initiated at irreducible water saturation, a scenario which is relevant for modern enhanced oil recovery techniques. The selected oil relative permeability models include classical and recent formulations and we consider their performance when (i) solely two-phase data are employed and/or (ii) two-and three-phase data are jointly used to render predictions of three-phase oil relative permeability, kro. We assess identifiability of model parameters through the Profile Likelihood (PL) technique. We rely on formal model discrimination criteria for a quantitative evaluation of the interpretive skill of each of the candidate models tested. We also evaluate the relative degree of likelihood associated with the competing models through a posterior probability weight and use Maximum Likelihood Bayesian model averaging to provide modelaveraged estimate of kro and the associated uncertainty bounds. Results show that assessing identifiability of uncertain model parameters on the basis of the available dataset can provide valuable information about the quality of the parameter estimates and can reduce computational costs by selecting solely identifiable models among available candidates.Eni SpA (Project "Microscale modeling of multiphase flow in porous media Micro - Flow") [OdL. 4310160993]24 month embargo; published online: 27 September 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

F52 Acoustic voice features in Huntington's disease in native English, Spanish and Polish speakers

Background Changes in voice are a characteristic symptom of Huntington’s disease (HD), and can be associated with lack of motor control (dysarthria) or cognitive impairment. Objective quantitative measures of voice that can be performed within the clinic or home may provide a method to monitor disease progression and help target interventions. Aims This study aimed to compare acoustic voice features from voice recordings in English, Spanish and Polish-speaking in people with HD and controls. Methods Voice was recorded using mobile devices (Samsung Tablets Tab A and S6 Lite, and Smartphone Huawei Mate Lite 10) in participants with HD and matched control groups across three cohorts: English- (n=29), Spanish- (n=36) and Polish- (n=25) speaking. Voice was recorded during sustained vowel phonation (/a:/), syllable repetitions (/pa/,/ta/,/ka/,/pataka/,/pati/), and reading of a language-specific passage (English,1 Spanish,2 Polish3). Acoustic voice features were estimated and compared across cohorts and biological sex. Statistical analysis was performed using linear mixed models. Results Significant differences between HD and control participants were observed for the standard deviation of fundamental frequency, harmonics-to-noise ratio, jitter and shimmer (Figure 1). Effect of biological sex was observed on all features examined

Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease

Study Objectives: Wearable devices, monitoring sleep stages and heart rate (HR), bring the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington’s disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD. Methods: Ten participants with manifest HD wore a FB during overnight hospital-based PSG, and for nine of these participants continued to wear the FB for seven nights at home. Sleep stages (30s epochs) and minute-by-minute HR were extracted and compared against PSG data. Results: FB-estimated total sleep and wake times, and sleep stage times were in good agreement with PSG, with intra-class correlations 0.79-0.96. However, poor agreement was observed for Wake After Sleep Onset, and the number of awakenings. FB detected wake with 68.6±15.5% sensitivity and 93.7±2.5% specificity, rapid eye movement (REM) sleep with high sensitivity and specificity (78.7±31.9%, 95.6±2.3%), and deep sleep with lower sensitivity but high specificity (56.4±28.8%, 95.0±4.8%). FB HR was strongly correlated with PSG, and the mean absolute error between FB and PSG HR data was 1.16 ± 0.42 bpm. At home, longer sleep and shorter wake times were observed compared to hospital data, while percentage sleep stage times were consistent with hospital data. Conclusions: Results suggest the potential for long-term monitoring of sleep patterns using wrist-worn wearable devices as part of symptom management in HD