Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the ...gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.
Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of ...community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = -0.9929 to 0.9986,
< 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.
Studies have shown that about half of the injuries sustained during long-distance running involve the knee. Cadence (steps per minute) has been identified as a factor that is strongly associated with ...these running-related injuries, making it a worthwhile candidate for further study. As such, it is critical for long-distance runners to minimize their risk of injury by running at an appropriate running cadence. In this paper, we present the results of a study on the feasibility and usability of RunningCoach, a mobile health (mHealth) system that remotely monitors running cadence levels of runners in a continuous fashion, among other variables, and provides immediate feedback to runners in an effort to help them optimize their running cadence.
We present a smartphone-based system for real-time tele-monitoring of physical activity in patients with chronic heart-failure (CHF). We recently completed a pilot study with 15 subjects to evaluate ...the feasibility of the proposed monitoring in the real world and examine its requirements, privacy implications, usability, and other challenges encountered by the participants and healthcare providers. Our tele-monitoring system was designed to assess patient activity via minute-by-minute energy expenditure (EE) estimated from accelerometry. In addition, we tracked relative user location via global positioning system (GPS) to track outdoors activity and measure walking distance. The system also administered daily surveys to inquire about vital signs and general cardiovascular symptoms. The collected data were securely transmitted to a central server where they were analyzed in real time and were accessible to the study medical staff to monitor patient health status and provide medical intervention if needed. Although the system was designed for tele-monitoring individuals with CHF, the challenges, privacy considerations, and lessons learned from this pilot study apply to other chronic health conditions, such as diabetes and hypertension, that would benefit from continuous monitoring through mobile-health (mHealth) technologies.
The current healthcare model in the United States of America (US) is reactive in nature. That is, individuals usually seek medical attention after symptoms manifest. In 2015, the total cost of ...healthcare in the US was $3.2 trillion (17.8% of US Gross Domestic Product), which amounts to $9,990 per capita. In the same year, the 30-days all-condition rate of unplanned rehospitalizations in patients in the Medicare fee-for-service program was around 17.9%; and between October 1, 2003 and December 31, 2003, 3.5% of patients in the same program died within 30 days of initial discharge. Alternatively, a healthcare model that utilizes medical intervention based on personalized predictions of the patient's clinical status and possible deterioration could potentially decrease costs, unplanned rehospitalizations and mortality rates. This model also has the potential to improve the overall quality of care. We refer to this model as the predictive healthcare model. In this dissertation, we examine three outstanding challenges towards fully realizing the predictive healthcare model as the prevalent care model. Namely, i) we investigate means to streamline the costly longitudinal epidemiological studies using remote mobile monitoring and introduce the Berkeley Telemonitoring project; ii) we investigate the privacy challenge that is particular to the remote monitoring model and introduce the Private Disclosure of Information (PDI) semantic privacy model; and iii) we investigate the problem of publication bias in empirical sciences (including biomedicine) that hinders the credibility of empirical scientific findings and introduce a statistical test that detects bias in a sample of scientific publications which utilize the Student t-test.
We present a novel framework, called Private Disclosure of Information (PDI), which is aimed to prevent an adversary from inferring certain sensitive information about subjects using the data that ...they disclosed during communication with an intended recipient. We show cases where it is possible to achieve perfect privacy regardless of the adversary's auxiliary knowledge while preserving full utility of the information to the intended recipient and provide sufficient conditions for such cases. We also demonstrate the applicability of PDI on a real-world data set that simulates a health tele-monitoring scenario.
Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of ...community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.
Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically-developing (TD) peers are visible to the eye, but quantifications of those differences outside of the ...gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3-16 years of age underwent eight walking/running activities, including five 25 meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-minute walk test (6MWT), a 100 meters fast-walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.
A Telemonitoring Framework for Android Devices Aranki, Daniel; Kurillo, Gregorij; Mani, Adarsh ...
2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE),
06/2016
Conference Proceeding
Real-time telemonitoring of patient's well-being through various wearable sensors and other electronic accessories holds promise to provide better quality healthcare at lower costs. The design and ...implementation of telemonitoring applications is however still a cumbersome process as it requires implementation of user interfaces, data acquisition, data storage, and proper security and privacy mechanisms using various APIs. This process requires a high level of experience in software development and design, as well as a certain level of knowledge in the healthcare domain. The multi-disciplinary nature of such applications limits the growth of telemonitoring. In addition, a large number of applications aim to use smartphone-based monitoring, which adds an extra level of complexity due to the fault-prone nature of such systems. In this paper, we describe a general-purpose framework that can be used to easily implement telemonitoring applications on Android-enabled devices.
This study retrospectively reviews an aggressive multidisciplinary approach to the treatment of massive pulmonary embolism, centering on rapid diagnosis with contrast-enhanced computed tomography of ...the chest to define the location and degree of clot burden and transthoracic echocardiography to document right ventricular strain followed by immediate surgical intervention when appropriate.
Between October 1999 through February 2004, 47 patients (30 men and 17 women; median age, 58 years; age range, 24–86 years) underwent emergency surgical embolectomy for massive central pulmonary embolism. The indications for surgical intervention were (1) contraindications to thrombolysis (21/47 45%), (2) failed medical treatment (5/47 10%), and (3) right ventricular dysfunction (15/47 32%). Preoperatively, 12 (26%) of 47 patients were in cardiogenic shock, and 6 (11%) of 47 were in cardiac arrest.
There were 3 (6%) operative deaths, 2 with preoperative cardiac arrest; 2 of these 3 patients required a right ventricular assist device. In 38 (81%) patients a caval filter was placed intraoperatively. Median length of stay was 11 days (range, 3–75 days). Median follow-up was 27 months (range, 2–50 months); follow-up was 100% complete in surviving patients. There were 6 (12%) late deaths, 5 of which were from metastatic cancer. Actuarial survival at 1 and 3 years’ follow-up was 86% and 83%, respectively.
An aggressive approach to large pulmonary embolus, including rapid diagnosis and prompt surgical intervention, has improved results with surgical embolectomy. We now perform surgical pulmonary embolectomy not only in patients with large central clot burden and hemodynamic compromise but also in hemodynamically stable patients with right ventricular dysfunction documented by means of echocardiography.
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