Development and evaluation of the ARM algorithm: A novel approach to quantify musculoskeletal disorder risk factors in manual wheelchair users in the real world.

This study aimed to develop and evaluate the ARM (arm repetitive movement) algorithm using inertial measurement unit (IMU) data to assess repetitive arm motion in manual wheelchair (MWC) users in real-world settings. The algorithm was tested on community data from four MWC users with spinal cord injury and compared with video-based analysis. Additionally, the algorithm was applied to in-home and free-living environment data from two and sixteen MWC users, respectively, to assess its utility in quantifying differences across activities of daily living and between dominant and non-dominant arms. The ARM algorithm accurately estimated active and resting times (>98%) in the community and confirmed asymmetries between dominant and non-dominant arm usage in in-home and free-living environment data. Analysis of free-living environment data revealed that the total resting bout time was significantly longer (P = 0.049) and total active bout time was significantly shorter (P = 0.011) for the non-dominant arm. Analysis of active bouts longer than 10 seconds showed higher total time (P = 0.015), average duration (P = 0.026), and number of movement cycles per bout (P = 0.020) for the dominant side. These findings support the feasibility of using the IMU-based ARM algorithm to assess repetitive arm motion and monitor shoulder disorder risk factors in MWC users during daily activities.

Classification of human walking context using a single-point accelerometer.

Real-world walking data offers rich insights into a person's mobility. Yet, daily life variations can alter these patterns, making the data challenging to interpret. As such, it is essential to integrate context for the extraction of meaningful information from real-world movement data. In this work, we leveraged the relationship between the characteristics of a walking bout and context to build a classification algorithm to distinguish between indoor and outdoor walks. We used data from 20 participants wearing an accelerometer on the thigh over a week. Their walking bouts were isolated and labeled using GPS and self-reporting data. We trained and validated two machine learning models, random forest and ensemble Support Vector Machine, using a leave-one-participant-out validation scheme on 15 subjects. The 5 remaining subjects were used as a testing set to choose a final model. The chosen model achieved an accuracy of 0.941, an F1-score of 0.963, and an AUROC of 0.931. This validated model was then used to label the walks from a different dataset with 15 participants wearing the same accelerometer. Finally, we characterized the differences between indoor and outdoor walks using the ensemble of the data. We found that participants walked significantly faster, longer, and more continuously when walking outdoors compared to indoors. These results demonstrate how movement data alone can be used to obtain accurate information on important contextual factors. These factors can then be leveraged to enhance our understanding and interpretation of real-world movement data, providing deeper insights into a person's health.

Challenges and advances in the use of wearable sensors for lower extremity biomechanics.

The use of wearable sensors for the collection of lower extremity biomechanical data is increasing in popularity, in part due to the ease of collecting data and the ability to capture movement outside of traditional biomechanics laboratories. Consequently, an increasing number of researchers are facing the challenges that come with utilizing the data captured by wearable sensors. These challenges include identifying/calculating meaningful measures from unfamiliar data types (measures of acceleration and angular velocity instead of positions and joint angles), defining sensor-to-segment alignments for calculating traditional biomechanics metrics, using reduced sensor sets and machine learning to predict unmeasured signals, making decisions about when and how to make algorithms freely available, and developing or replicating methods to perform basic processing tasks such as recognizing activities of interest or identifying gait events. In this perspective article, we present our own approaches to common challenges in lower extremity biomechanics research using wearable sensors and share our perspectives on approaching several of these challenges. We present these perspectives with examples that come mostly from gait research, but many of the concepts also apply to other contexts where researchers may use wearable sensors. Our goal is to introduce common challenges to new users of wearable sensors, and to promote dialogue amongst experienced users towards best practices.

Game-Day Pitch and Throw Count Feasibility Using a Single Sensor to Quantify Workload in Youth Baseball Players.

Background: Pitch count recommendations are used to reduce injury risk in youth baseball pitchers and are based chiefly on expert opinion, with limited scientific support. Furthermore, they only account for pitches thrown against a hitter and do not include the total number of throws on the day a player pitched. Currently, counts are recorded manually. Purpose: To provide a method using a wearable sensor to quantify total throws per game that is compliant with Little League Baseball rules and regulations. Study Design: Descriptive laboratory study. Methods: Eleven male baseball players (age, 10-11 years) from an 11U (players 11 years and younger) competitive travel team were evaluated over a single summer season. An inertial sensor was placed above the midhumerus of the throwing arm and was worn during baseball games across the season. A throw identification algorithm capturing all throws and reporting linear acceleration and peak linear acceleration was used to quantify throwing intensity. Pitching charts were collected and used to verify actual pitches thrown against a hitter in a game versus all other throws identified. Results: A total of 2748 pitches and 13,429 throws were captured. On the day a player pitched, he averaged 36 ± 18 pitches (23%) and 158 ± 106 total throws (pitches in game as well as all warm-up pitches and other throws during game). In comparison, on a day a player did not pitch, he averaged 119 ± 102 throws. Across all pitchers, 32% of all throws were low intensity, 54% were medium intensity, and 15% were high intensity. The player with one of the highest percentages of high-intensity throws did not pitch as their primary position, while the 2 players who pitched most often had the lowest percentages. Conclusion: Total throw count can be successfully quantified using a single inertial sensor. Total throws tended to be higher on days a player pitched compared with regular game days without pitching. Clinical Relevance: This study provides a fast, feasible, and reliable method to obtain pitch and throw counts so that more rigorous research on contributing factors to arm injury in the youth athlete can be achieved.

Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits.

Inertial measurement units (IMUs) offer an attractive way to study human lower-limb kinematics without traditional laboratory constraints. We present an error-state Kalman filter method to estimate 3D joint angles, joint angle ranges of motion, stride length, and step width using data from an array of seven body-worn IMUs. Importantly, this paper contributes a novel joint axis measurement correction that reduces joint angle drift errors without assumptions of strict hinge-like joint behaviors of the hip and knee. We evaluate the method compared to two optical motion capture methods on twenty human subjects performing six different types of walking gait consisting of forward walking (at three speeds), backward walking, and lateral walking (left and right). For all gaits, RMS differences in joint angle estimates generally remain below 5 degrees for all three ankle joint angles and for flexion/extension and abduction/adduction of the hips and knees when compared to estimates from reflective markers on the IMUs. Additionally, mean RMS differences in estimated stride length and step width remain below 0.13 m for all gait types, except stride length during slow walking. This study confirms the method's potential for non-laboratory based gait analysis, motivating further evaluation with IMU-only measurements and pathological gaits.

Using Sensors for Player Development: Assessing Biomechanical Factors Related to Pitch Command and Velocity.

Pitching biomechanical research is highly focused on injury prevention with little attention to how biomechanical data can facilitate skill development. The overall purpose of this study was to explore how sensor-derived segment kinematics and timing relate to command and ball velocity during baseball pitching. We used a cross-sectional design to analyze a series of pitches thrown from 10 collegiate baseball pitchers. We collected biomechanical data from six inertial sensors, subjective command from the pitchers, and ball velocity from a radar device. Stepwise regression analyses were used to explore biomechanical variables associated with command for all pitches and ball velocity for fastballs only. We found that only peak forearm linear acceleration was significantly associated with command, whereas several segment kinematic measures were significantly associated with ball velocity. Our results suggest that different biomechanical variables are linked to specific pithing skills. Our findings suggest that end-effector (forearm) movement is more important for pitch command, whereas proximal-to-distal (pelvis, trunk, upper arm, forearm) segmental movement is important for ball velocity.

Investigating walking speed variability of young adults in the real world.

BACKGROUND: Walking speed strongly correlates with health outcomes, making accurate assessment essential for clinical evaluations. However, assessments tend to be conducted over short distances, often in a laboratory or clinical setting, and may not capture natural walking behavior. To address this gap, the following questions are investigated in this work: Is walking speed significantly influenced by the continuity and duration of a walking bout? Can preferred walking speed be inferred by grouping walking bouts using duration and continuity? METHODS: We collected two weeks of continuous data from fifteen healthy young adults using a thigh-worn accelerometer and a heart rate monitor. Walking strides were identified and grouped into walking periods. We quantified the duration and the continuity of each walking period. Continuity is used to parameterize changes in stepping rate related to pauses during a bout of walking. Finally, we analyzed the influence of duration and continuity on estimates of stride speed, and examined how the distribution of walking speed varies depending on different walking modes (defined by duration and continuity). RESULTS: We found that continuity and duration can be used to explain some of the variability in real-world walking speed (p<0.001). Speeds estimated from long continuous walks with many strides (42% of all recorded strides) had the lowest standard deviation. Walking speed during these bouts was 1.41ms-1 (SD = 0.26ms-1). SIGNIFICANCE: Walking behavior in the real world is largely variable. Features of real-world walks, like duration and continuity, can be used to explain some of the variability observed in walking speed. As such, we recommend using long continuous walks to confidently isolate the preferred walking behavior of an individual.

Assessment of a powered ankle exoskeleton on human stability and balance.

Wearable robotic systems, such as exoskeletons, are designed to assist human motion; however, they are typically only studied during level walking. Before exoskeletons are broadly integrated into unstructured environments, it will be important to evaluate exoskeletons in a broader set of relevant tasks. A balance beam traverse was used to represent a constrained foot placement task for examining balance and stability. Participants (n = 17) completed the task in their own shoes (Pre-Exoskeleton and Post-Exoskeleton trials), and when wearing a lower-limb exoskeleton (Dephy ExoBoot) in both powered and unpowered states. Data were collected via inertial measurement units (on the torso and feet) and analyzed on a pooled level (with data from all participants) and on an individual level (participant-specific confidence intervals). When examining pooled data, it was observed that the exoskeleton had mixed effects on stride stability metrics. When compared to the Post-Exoskeleton shoe control, it was observed that stride duration was increased when wearing the exoskeleton (both powered and unpowered states), while normalized stride length and stride speed were not affected. Despite the changes in stride stability, overall balance (as measured by torso sway) remained unaffected by exoskeleton state. On an individual level, it was observed that not all participants followed these general trends, and within each metric, some increased, some decreased, and some had no change in the Powered Exoskeleton condition when compared to the Post-Exoskeleton Shoe condition: normalized stride length (0% increased, 12% decreased, 88% no change), stride duration (35% increased, 0% decreased, 65% no change), and torso sway (0% increased, 12% decreased, 88% no change). Our findings suggest that the lower-limb exoskeleton evaluated can be used during tasks that require balancing, and we recommend that balancing tasks be included in standards for exoskeleton evaluation.

Risk Factors for COVID-19 in College Students Identified by Physical, Mental, and Social Health Reported During the Fall 2020 Semester: Observational Study Using the Roadmap App and Fitbit Wearable Sensors.

BACKGROUND: The COVID-19 pandemic triggered a seismic shift in education to web-based learning. With nearly 20 million students enrolled in colleges across the United States, the long-simmering mental health crisis in college students was likely further exacerbated by the pandemic. OBJECTIVE: This study leveraged mobile health (mHealth) technology and sought to (1) characterize self-reported outcomes of physical, mental, and social health by COVID-19 status; (2) assess physical activity through consumer-grade wearable sensors (Fitbit); and (3) identify risk factors associated with COVID-19 positivity in a population of college students prior to release of the vaccine. METHODS: After completing a baseline assessment (ie, at Time 0 [T0]) of demographics, mental, and social health constructs through the Roadmap 2.0 app, participants were instructed to use the app freely, wear the Fitbit, and complete subsequent assessments at T1, T2, and T3, followed by a COVID-19 assessment of history and timing of COVID-19 testing and diagnosis (T4: ~14 days after T3). Continuous measures were described using mean (SD) values, while categorical measures were summarized as n (%) values. Formal comparisons were made on the basis of COVID-19 status. The multivariate model was determined by entering all statistically significant variables (P<.05) in univariable associations at once and then removing one variable at a time through backward selection until the optimal model was obtained. RESULTS: During the fall 2020 semester, 1997 participants consented, enrolled, and met criteria for data analyses. There was a high prevalence of anxiety, as assessed by the State Trait Anxiety Index, with moderate and severe levels in 465 (24%) and 970 (49%) students, respectively. Approximately one-third of students reported having a mental health disorder (n=656, 33%). The average daily steps recorded in this student population was approximately 6500 (mean 6474, SD 3371). Neither reported mental health nor step count were significant based on COVID-19 status (P=.52). Our analyses revealed significant associations of COVID-19 positivity with the use of marijuana and alcohol (P=.02 and P=.046, respectively) and with lower belief in public health measures (P=.003). In addition, graduate students were less likely and those with ≥20 roommates were more likely to report a COVID-19 diagnosis (P=.009). CONCLUSIONS: Mental health problems were common in this student population. Several factors, including substance use, were associated with the risk of COVID-19. These data highlight important areas for further attention, such as prioritizing innovative strategies that address health and well-being, considering the potential long-term effects of COVID-19 on college students. TRIAL REGISTRATION: ClinicalTrials.gov NCT04766788; https://clinicaltrials.gov/ct2/show/NCT04766788. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/29561.

Inertial Measurement Unit-Derived Ergonomic Metrics for Assessing Arm Use in Manual Wheelchair Users With Spinal Cord Injury: A Preliminary Report.

BACKGROUND: Individuals with spinal cord injury (SCI) who use manual wheelchairs (MWCs) have a higher rate of rotator cuff pathology progression than able-bodied individuals. OBJECTIVES: This study aimed to test the ability of risk and recovery metrics of arm use to differentiate between (1) MWC users with SCI and matched able-bodied participants (cross-sectional matched-sample study) and (2) MWC users with rotator cuff pathology progression over 1 year from those without pathology progression (longitudinal study). METHODS: Thirty-four MWC users and 34 age- and sex-matched able-bodied individuals were recruited. Upper arm risk (humeral elevation >60°) and recovery (static ≥5 seconds and humeral elevation <40°) metrics were calculated from wireless inertial measurement units (IMUs) worn on the upper arms and torso in the free-living environment. Two separate magnetic resonance imaging studies were completed and assessed for a subset of 16 MWC users approximately 1 year apart. RESULTS: The frequency of risk events (p = .019), summated duration of recovery events (p = .025), and duration of each recovery event (p = .003) were higher for MWC users than able-bodied participants. The summated duration of risk events (p = .047), frequency of risk events (p = .027), and risk to recovery ratio (p = .02) were higher and the summated duration of recovery events (p = .036) and frequency of recovery events (p = .047) were lower for MWC users with rotator cuff pathology progression (n = 5) compared to those without progression (n = 11). CONCLUSION: IMU-derived metrics quantifying arm use at postures >60° and risk to recovery ratios may provide insights of potential risk factors for rotator cuff pathology progression.

Duration of Static and Dynamic Periods of the Upper Arm During Daily Life of Manual Wheelchair Users and Matched Able-Bodied Participants: A Preliminary Report.

Background: Manual wheelchair (MWC) users with spinal cord injuries (SCI) are at a significantly higher risk of experiencing rotator cuff pathology than able-bodied individuals. A deeper understanding of where the arm is used dynamically within the humeral workspace during daily life may help explain why MWC users have higher shoulder pathology rates than able-bodied individuals. The purpose of this study was to report the daily percentage and consecutive durations MWC users and matched able-bodied individuals (controls) spent static and dynamic across the humeral elevation workspace. Methods: MWC users with SCI and controls wore three inertial measurement units on their bilateral arms and torso for 1 or 2 days. The percentages of time and average consecutive duration individuals were static or dynamic while in five humeral elevation ranges (0-30°, 30-60°, 60-90°, 90-120°, and >120°) were calculated and compared between cohorts. Results: Forty-four MWC users (10 females, age: 42.8 ± 12.0, time since injury: 12.3 ± 11.5) and 44 age- and sex-matched controls were enrolled. The MWC cohort spent significantly more time dynamic in 60-90° (p = 0.039) and 90-120° (p = 0.029) and had longer consecutive dynamic periods in 30-60° (p = 0.001), 60-90° (p = 0.027), and 90-120° (p = 0.043) on the dominant arm. The controls spent significantly more time dynamic in 0-30° of humeral elevation (p < 0.001) on both arms. Although the average consecutive static durations were comparable between cohorts across all humeral elevation ranges, the MWC cohort spent a significantly higher percentage of their day static in 30-60° of humeral elevation than controls (dominant: p = 0.001, non-dominant: p = 0.01). The MWC cohort had a moderate association of increased age with decreased time dynamic in 30-60° for both arms. Discussion: Remote data capture of arm use during daily life can aid in understanding how arm function relates to shoulder pathology that follows SCI and subsequent MWC use. MWC users spent more time dynamic in higher elevations than controls, and with age, dynamic arm use decreased in the 30-60° humeral elevation range. These results may exemplify effects of performing activities from a seated position and of age on mobility.

Error-state Kalman filter for lower-limb kinematic estimation: Evaluation on a 3-body model.

Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet.

Humeral elevation workspace during daily life of adults with spinal cord injury who use a manual wheelchair compared to age and sex matched able-bodied controls.

Shoulder pain and pathology are extremely common for individuals with spinal cord injuries (SCI) who use manual wheelchairs (MWC). Although risky humeral kinematics have been measured during wheelchair-based activities performed in the lab, little is known about arm kinematics in the free-living environment. The purpose of this study was to measure the humeral elevation workspace throughout a typical day for individuals with SCI who use a MWC and matched able-bodied controls. Thirty-four individuals with SCI who use a MWC (42.7±12.7 years of age, 28 males/6 females, C6-L1) and 34 age-and sex-matched controls were enrolled. Participants wore three inertial measurement units (IMU) on their upper arms and torso for one to two days. Humeral elevation angles were estimated and the percentage of time individuals spent in five elevation bins (0-30°, 30-60°, 60-90°, 90-120°, and 120-180°) were calculated. For both arms, the SCI cohort spent a significantly lower percentage of the day in 0-30° of humeral elevation (Dominant: SCI = 15.7±12.6%, Control = 32.1±15.6%, p<0.0001; Non-Dominant: SCI = 21.9±17.8%, Control = 34.3±15.5%, p = 0.001) and a significantly higher percentage of time in elevations associated with tendon compression (30-60° of humeral elevation, Dominant: SCI = 62.8±14.4%, Control = 49.9.1±13.0%, p<0.0001; Non-Dominant: SCI = 58.8±14.9%, Control = 48.3±13.6%, p = 0.003) than controls. The increased percentage of time individuals with SCI spent in elevations associated with tendon compression may contribute to increased shoulder pathology. Characterizing the humeral elevation workspace utilized throughout a typical day may help in understanding the increased prevalence of shoulder pain and pathology in individuals with SCI who use MWCs.

Quantifying warfighter performance during a bounding rush (prone-sprinting-prone) maneuver.

A single sacrum mounted inertial measurement unit (IMU) was employed to analyze warfighter performance on a bounding rush (prone-sprinting-prone) task. Thirty-nine participants (23M/16F) performed a bounding rush task consisting of four bounding rush cycles. The sacrum mounted IMU recorded angular velocity and acceleration data were used to provide estimates of sacral velocity and position. Individual rush cycles were parsed into three principal movement phases; namely, the get up, sprint, and get down phases. The timing of each phase was analyzed, averaged for each participant, and compared to the overall rush cycle time using regression analysis. A cluster analysis further reveals differences between high and low performers. Get down time was most predictive of bounding rush performance (R2 = 0.75) followed by get up time (R2 = 0.58) and sprint time (R2 = 0.40). Comparing high and low performers, the get down time exhibited nearly twice the effect on mean rush cycle time compared to get up time (effect size of -2.61 to -1.46, respectively). Overall, this IMU-based method reveals key features of the bounding rush that govern performance. Consequently, this objective method may support future training regimens and performance standards for military recruits, and parallel applications for athletes.

Impact of the Support, Educate, Empower Personalized Glaucoma Coaching Program Pilot Study on Eye Drop Instillation Technique and Self-Efficacy.

PURPOSE: To assess the Support, Educate, Empower (SEE) personalized glaucoma coaching program impact on (1) eye drop instillation technique and (2) eye drop instillation self-efficacy. DESIGN: Prospective pre-post pilot study. PARTICIPANTS: Patients with a diagnosis of glaucoma or ocular hypertension taking ≥1 glaucoma medication, ≥40 years old, spoke English, self-administered their eye drops, and ≤80% adherent to their glaucoma medication by electronic monitoring. METHODS: Eye drop administration was video recorded before the first SEE in-person coaching session, which included teaching eye drop instillation techniques using a motivational interviewing-based approach. At the third and final in-person counseling session 6 months later, eye drop administration was video recorded. Participants' self-efficacy was assessed using the validated Eye Drop Technique Self-Efficacy Scale (EDTSES) survey at baseline and 1 month after completion of the program. Before and after intervention videos were assessed by an observer masked to time point. Before versus after intervention comparisons were made using McNemar's and paired t tests. MAIN OUTCOME MEASURES: The main outcome was change in participants' eye drop instillation technique as measured by (1) accuracy of an eye drop landing on the eye, (2) ability to instill an eye drop on the first attempt, and (3) contaminating the bottle by contact with ocular surface, eyelashes, and skin. The secondary outcome measure was before versus after change in the EDTSES score (6 items, each assessed on a 3-point Likert scale, with higher scores indicating better self-efficacy). RESULTS: Thirty-nine participants completed the SEE intervention, 38 with before and after EDTSES scores and 31 with video recordings. Six of 31 participants instilling drops outside the eye before intervention improved their technique after intervention, whereas 2 participants worsened (P = 0.157). From before to after intervention, participants demonstrated significant improvement in not touching the ocular surface (P = 0.046), the eyelashes (P = 0.020), or the skin (P = 0.025) with the bottle tip. A significant increase was found in eye drop instillation self-efficacy from an average score of 2.6 (standard deviation [SD], 0.3) to 2.8 (SD, 0.2) on the EDTSES score (P = 0.007). CONCLUSIONS: The SEE program significantly decreased eye drop bottle contamination, increased eye drop instillation self-efficacy, and demonstrated an insignificant increase in ability to instill drops successfully and accurately.

Quantifying Throw Counts and Intensities Throughout a Season in Youth Baseball Players: A Pilot Study.

Overuse injuries in youth baseball players due to throwing are at an all-time high. Traditional methods of tracking player throwing load only count in-game pitches and therefore leave many throws unaccounted for. Miniature wearable inertial sensors can be used to capture motion data outside of the lab in a field setting. The objective of this study was to develop a protocol and algorithms to detect throws and classify throw intensity in youth baseball athletes using a single, upper arm-mounted inertial sensor. Eleven participants from a youth baseball team were recruited to participate in the study. Each participant was given an inertial measurement unit (IMU) and was instructed to wear the sensor during any baseball activity for the duration of a summer season of baseball. A throw identification algorithm was developed using data from a controlled data collection trial. In this report, we present the throw identification algorithm used to identify over 17,000 throws during the 2-month duration of the study. Data from a second controlled experiment were used to build a support vector machine model to classify throw intensity. Using this classification algorithm, throws from all participants were classified as being "low," "medium," or "high" intensity. The results demonstrate that there is value in using sensors to count every throw an athlete makes when assessing throwing load, not just in-game pitches.

Estimating Walking Speed in the Wild.

An individual's physical activity substantially impacts the potential for prevention and recovery from diverse health issues, including cardiovascular diseases. Precise quantification of a patient's level of day-to-day physical activity, which can be characterized by the type, intensity, and duration of movement, is crucial for clinicians. Walking is a primary and fundamental physical activity for most individuals. Walking speed has been shown to correlate with various heart pathologies and overall function. As such, it is often used as a metric to assess health performance. A range of clinical walking tests exist to evaluate gait and inform clinical decision-making. However, these assessments are often short, provide qualitative movement assessments, and are performed in a clinical setting that is not representative of the real-world. Technological advancements in wearable sensing and associated algorithms enable new opportunities to complement in-clinic evaluations of movement during free-living. However, the use of wearable devices to inform clinical decisions presents several challenges, including lack of subject compliance and limited sensor battery life. To bridge the gap between free-living and clinical environments, we propose an approach in which we utilize different wearable sensors at different temporal scales and resolutions. Here, we present a method to accurately estimate gait speed in the free-living environment from a low-power, lightweight accelerometer-based bio-logging tag secured on the thigh. We use high-resolution measurements of gait kinematics to build subject-specific data-driven models to accurately map stride frequencies extracted from the bio-logging system to stride speeds. The model-based estimates of stride speed were evaluated using a long outdoor walk and compared to stride parameters calculated from a foot-worn inertial measurement unit using the zero-velocity update algorithm. The proposed method presents an average concordance correlation coefficient of 0.80 for all subjects, and 97% of the error is within ±0.2m· s -1. The approach presented here provides promising results that can enable clinicians to complement their existing assessments of activity level and fitness with measurements of movement duration and intensity (walking speed) extracted at a week time scale and in the patients' free-living environment.

Medication Adherence and Liquid Level Tracking System for Healthcare Provider Feedback.

A common problem for healthcare providers is accurately tracking patients' adherence to medication and providing real-time feedback on the management of their medication regimen. This is a particular problem for eye drop medications, as the current commercially available monitors focus on measuring adherence to pills, and not to eye drops. This work presents an intelligent bottle sleeve that slides onto a prescription eye drop medication bottle. The intelligent sleeve is capable of detecting eye drop use, measuring fluid level, and sending use information to a healthcare team to facilitate intervention. The electronics embedded into the sleeve measure fluid level, dropper orientation, the state of the dropper top (on/off), and rates of angular motion during an application. The sleeve was tested with ten patients (age ≥65) and successfully identified and timestamped 94% of use events. On-board processing enabled event detection and the measurement of fluid levels at a 0.4 mL resolution. These data were communicated to the healthcare team using Bluetooth and Wi-Fi in real-time, enabling rapid feedback to the subject. The healthcare team can therefore monitor a log of medication use behavior to make informed decisions on treatment or support for the patient.

Workload Differentiates Breast Surgical Procedures: NSM Associated with Higher Workload Demand than SSM.

BACKGROUND: Breast surgery has evolved with more focus on improving cosmetic outcomes, which requires increased operative time and technical complexity. Implications of these technical advances in surgery for the surgeon are unclear, but they may increase intraoperative demands, both mentally and physically. We prospectively evaluated mental and physical demand across breast surgery procedures, and compared surgeon ergonomic risk between nipple-sparing (NSM) and skin-sparing mastectomy (SSM) using subjective and objective measures. METHODS: From May 2017 to July 2017, breast surgeons completed modified NASA-Task Load Index (TLX) workload surveys after cases. From January 2018 to July 2018, surgeons completed workload surveys and wore inertial measurement units to evaluate their postures during NSM and SSM cases. Mean angles of surgical postures, ergonomic risk, survey items, and patient factors were analyzed. RESULTS: Procedural duration was moderately related to surgeon frustration, mental and physical demand, and fatigue (p < 0.001). NSMs were rated 23% more physically demanding (M = 13.3, SD = 4.3) and demanded 28% more effort (M = 14.4, SD = 4.6) than SSMs (M = 10.8, SD = 4.7; M = 11.8, SD = 5.0). Incision type was a contributing factor in workload and procedural difficulty. Left arm mean angle was significantly greater for NSM (M = 30.1 degrees, SD = 6.6) than SSMs (M = 18.2 degrees, SD = 4.3). A higher musculoskeletal disorder risk score for the trunk was significantly associated with higher surgeon physical workload (p = 0.02). CONCLUSION: Nipple-sparing mastectomy required the highest surgeon-reported workload of all breast procedures, including physical demand and effort. Objective measures identified the surgeons' left upper arm as being at the greatest risk for a work-related musculoskeletal disorder, specifically from performing NSMs.

Effect of IMU Design on IMU-Derived Stride Metrics for Running.

Researchers employ foot-mounted inertial measurement units (IMUs) to estimate the three-dimensional trajectory of the feet as well as a rich array of gait parameters. However, the accuracy of those estimates depends critically on the limitations of the accelerometers and angular velocity gyros embedded in the IMU design. In this study, we reveal the effects of accelerometer range, gyro range, and sampling frequency on gait parameters (e.g., distance traveled, stride length, and stride angle) estimated using the zero-velocity update (ZUPT) method. The novelty and contribution of this work are that it: (1) quantifies these effects at mean speeds commensurate with competitive distance running (up to 6.4 m/s); (2) identifies the root causes of inaccurate foot trajectory estimates obtained from the ZUPT method; and (3) offers important engineering recommendations for selecting accurate IMUs for studying human running. The results demonstrate that the accuracy of the estimated gait parameters generally degrades with increased mean running speed and with decreased accelerometer range, gyro range, and sampling frequency. In particular, the saturation of the accelerometer and/or gyro induced during running for some IMU designs may render those designs highly inaccurate for estimating gait parameters.