JMIR biomedical engineering最新文献

Background: Parkinson disease (PD) is a neurodegenerative disease that has a wide range of motor symptoms, such as tremor. Tremors are involuntary movements that occur in rhythmic oscillations and are typically categorized into rest tremor or action tremor. Action tremor occurs during voluntary movements and is a debilitating symptom of PD. As noninvasive interventions are limited, there is an ever-increasing need for an effective intervention for individuals experiencing action tremors. The Microsoft Emma Watch, a wristband with 5 vibrating motors, is a noninvasive, nonpharmaceutical intervention for tremor attenuation.

Objective: This pilot study investigated the use of the Emma Watch device to attenuate action tremor in people with PD.

Methods: The sample included 9 people with PD who were assessed on handwriting and hand function tasks performed on a digitized tablet. Tasks included drawing horizontal or vertical lines, tracing a star, spiral, writing "elelelel" in cursive, and printing a standardized sentence. Each task was completed 3 times with the Emma Watch programmed at different vibration intensities, which were counterbalanced: high intensity, low intensity (sham), and no vibration. Digital analysis from the tablet captured kinematic, dynamic, and spatial attributes of drawing and writing samples to calculate mathematical indices that quantify upper limb motor function. APDM Opal sensors (APDM Wearable Technologies) placed on both wrists were used to calculate metrics of acceleration and jerk. A questionnaire was provided to each participant after using the Emma Watch to gain a better understanding of their perspectives of using the device. In addition, drawings were compared to determine whether there were any visual differences between intensities.

Results: In total, 9 people with PD were tested: 4 males and 5 females with a mean age of 67 (SD 9.4) years. There were no differences between conditions in the outcomes of interest measured with the tablet (duration, mean velocity, number of peaks, pause time, and number of pauses). Visual differences were observed within a small subset of participants, some of whom reported perceived improvement. The majority of participants (8/9) reported the Emma Watch was comfortable, and no problems with the device were reported.

Conclusions: There were visually depicted and subjectively reported improvements in handwriting for a small subset of individuals. This pilot study was limited by a small sample size, and this should be taken into consideration with the interpretation of the quantitative results. Combining vibratory devices, such as the Emma Watch, with task specific training, or personalizing the frequency to one's individual tremor may be important steps to consider when evaluating the effect of vibratory devices on hand function or writing ability in future studies. While the E

Background: The incentive spirometer is a basic and common medical device from which electronic health care data cannot be directly collected. As a result, despite numerous studies investigating clinical use, there remains little consensus on optimal device use and sparse evidence supporting its intended benefits such as prevention of postoperative respiratory complications.

Objective: The aim of the study is to develop and test an add-on hardware device for data capture of the incentive spirometer.

Methods: An add-on device was designed, built, and tested using reflective optical sensors to identify the real-time location of the volume piston and flow bobbin of a common incentive spirometer. Investigators manually tested sensor level accuracies and triggering range calibrations using a digital flowmeter. A valid breath classification algorithm was created and tested to determine valid from invalid breath attempts. To assess real-time use, a video game was developed using the incentive spirometer and add-on device as a controller using the Apple iPad.

Results: In user testing, sensor locations were captured at an accuracy of 99% (SD 1.4%) for volume and 100% accuracy for flow. Median and average volumes were within 7.5% (SD 6%) of target volume sensor levels, and maximum sensor triggering values seldom exceeded intended sensor levels, showing a good correlation to placement on 2 similar but distinct incentive spirometer designs. The breath classification algorithm displayed a 100% sensitivity and a 99% specificity on user testing, and the device operated as a video game controller in real time without noticeable interference or delay.

Conclusions: An effective and reusable add-on device for the incentive spirometer was created to allow the collection of previously inaccessible incentive spirometer data and demonstrate Internet-of-Things use on a common hospital device. This design showed high sensor accuracies and the ability to use data in real-time applications, showing promise in the ability to capture currently inaccessible clinical data. Further use of this device could facilitate improved research into the incentive spirometer to improve adoption, incentivize adherence, and investigate the clinical effectiveness to help guide clinical care.

Background: Physiological motion of the lumbar spine is a topic of interest for musculoskeletal health care professionals since abnormal motion is believed to be related to lumbar complaints. Many researchers have described ranges of motion for the lumbar spine, but only few have mentioned specific motion patterns of each individual segment during flexion and extension, mostly comprising the sequence of segmental initiation in sagittal rotation. However, an adequate definition of physiological motion is still lacking. For the lower cervical spine, a consistent pattern of segmental contributions in a flexion-extension movement in young healthy individuals was described, resulting in a definition of physiological motion of the cervical spine.

Objective: This study aimed to define the lumbar spines' physiological motion pattern by determining the sequence of segmental contribution in sagittal rotation of each vertebra during maximum flexion and extension in healthy male participants.

Methods: Cinematographic recordings were performed twice in 11 healthy male participants, aged 18-25 years, without a history of spine problems, with a 2-week interval (time point T1 and T2). Image recognition software was used to identify specific patterns in the sequence of segmental contributions per individual by plotting segmental rotation of each individual segment against the cumulative rotation of segments L1 to S1. Intraindividual variability was determined by testing T1 against T2. Intraclass correlation coefficients were tested by reevaluation of 30 intervertebral sequences by a second researcher.

Results: No consistent pattern was found when studying the graphs of the cinematographic recordings during flexion. A much more consistent pattern was found during extension, especially in the last phase. It consisted of a peak in rotation in L3L4, followed by a peak in L2L3, and finally, in L1L2. This pattern was present in 71% (15/21) of all recordings; 64% (7/11) of the participants had a consistent pattern at both time points. Sequence of segmental contribution was less consistent in the lumbar spine than the cervical spine, possibly caused by differences in facet orientation, intervertebral discs, overprojection of the pelvis, and muscle recruitment.

Conclusions: In 64% (7/11) of the recordings, a consistent motion pattern was found in the upper lumbar spine during the last phase of extension in asymptomatic young male participants. Physiological motion of the lumbar spine is a broad concept, influenced by multiple factors, which cannot be captured in a firm definition yet.

Trial registration: ClinicalTrials.gov NCT03737227; https://clinicaltrials.gov/ct2/show/NCT03737227.

International registered report identifier (irrid): RR2-10.2196/14741.

Background: Measuring the amount of physical activity and its patterns using wearable sensor technology in real-world settings can provide critical insights into health status.

Objective: This study's aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology.

Methods: Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups.

Results: The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups.

Conclusions: Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users' walking activity and help researchers gain insights on users' health status.