Patrick Slade

Approximately 1.8 billion people experience hormonal changes caused by the menstrual cycle, which impacts work, school, and health factors like physical activity. Prior research shows high-level insights, such as reduced activity during symptomatic days, but the day-to-day relationship between the menstrual cycle and physical activity remains unclear. We performed a 28-day study monitoring physical activity in 26 healthy, naturally menstruating women using an inertial measurement unit on the shank. A validated model estimated energy expended per step. Physical activity was significantly higher during the early-follicular phase than the late follicular phase due to more steps. Energy expenditure was compared with open-source daily estradiol and progesterone data collected from different study cohorts of healthy, eumenorrheic women. Peak estradiol and physical activity showed a correlation ( r 2 = 0.64) when activity was shifted by 2 days, suggesting a delayed relationship. Precisely measuring movement with wearable sensors could help uncover menstrual cycle effects on activity and improve health management and workplace policies.

Globally, 340 million people have blindness or moderate to severe visual impairment (BVI)$^1$ which limits independent outdoor navigation$^2$ and negatively affects their health and quality of life$^{3,4}$. We surveyed 112 people with BVI and found that an ideal outdoor navigation aid must be able to perform turn-by-turn directions, path guidance, and obstacle detection and avoidance. Existing navigation tools such as white canes, guide dogs, and electronic travel aids often lack one or more of these criteria and may be expensive or inaccessible$^{5,6}$. Here we introduce Mobilio, a smartphone application that incorporates machine learning, sensor fusion algorithms, and personalized audio feedback to meet all of the outdoor navigation criteria. The reliability of the smartphone sensors and models used for navigation were assessed with engineering tests in representative navigation scenarios. We performed a series of experiments where Mobilio personalized audio feedback for participants with BVI (n = 14), guided them along an outdoor community path, and helped them navigate an obstacle course. Participants walking with Mobilio and a white cane reduced time to navigate a community path by 13 $\pm$ 3% and environmental contacts by 41 $\pm$ 5% compared to using Google Maps and a white cane. Mobilio achieved similar outdoor navigation reliability as a human guide. Participant surveys reported that Mobilio was easy to use, had a low perceived workload, and provided intuitive audio feedback. This work provides an accessible and personalized tool that may be an effective outdoor navigation aid to increase independence for people with BVI.

Globally, 340 million people have blindness or moderate to severe visual impairment (BVI)$^1$ which limits independent outdoor navigation$^2$ and negatively affects their health and quality of life$^{3,4}$. We surveyed 112 people with BVI and found that an ideal outdoor navigation aid must be able to perform turn-by-turn directions, path guidance, and obstacle detection and avoidance. Existing navigation tools such as white canes, guide dogs, and electronic travel aids often lack one or more of these criteria and may be expensive or inaccessible$^{5,6}$. Here we introduce Mobilio, a smartphone application that incorporates machine learning, sensor fusion algorithms, and personalized audio feedback to meet all of the outdoor navigation criteria. The reliability of the smartphone sensors and models used for navigation were assessed with engineering tests in representative navigation scenarios. We performed a series of experiments where Mobilio personalized audio feedback for participants with BVI (n = 14), guided them along an outdoor community path, and helped them navigate an obstacle course. Participants walking with Mobilio and a white cane reduced time to navigate a community path by 13 $\pm$ 3% and environmental contacts by 41 $\pm$ 5% compared to using Google Maps and a white cane. Mobilio achieved similar outdoor navigation reliability as a human guide. Participant surveys reported that Mobilio was easy to use, had a low perceived workload, and provided intuitive audio feedback. This work provides an accessible and personalized tool that may be an effective outdoor navigation aid to increase independence for people with BVI.

BACKGROUND: Walking requires managing competing movement goals shaped by the demands of everyday activity. Several important goals during overground walking include maintaining a desired walking speed, achieving an intended foot placement, preserving postural and locomotor balance, and reducing energy expenditure. Biomechanical simulation and motor control-based methods use goal-related cost functions or structured models to identify people’s movement priorities but are often restricted to simple movement scenarios and are currently impractical for broad deployment. METHODS: Here, we systematically vary four movement goals during an overground walking experiment, characterize the resulting trade-offs in spatiotemporal gait metrics, and show that these trade-offs are sufficient to estimate participants’ perceived ranking of movement goals. RESULTS: Gait metrics across participants became more heterogenous as combinations of movement goal prompts became more complex, suggesting person-specific movement strategies. Participants’ perceived prioritization of movement goals showed systematic associations with corresponding gait metrics, with the strongest relationships observed for visually guided goals such as balance and foot placement. Subject-agnostic regression models estimated the perceived order of importance of movement goals with 21% error, and subject-specific models reduced this error to 11%. CONCLUSIONS: This work establishes an approach for estimation of movement priorities in young, healthy adults using simple gait metrics and provides a framework that could inform future studies beyond the laboratory.

Abstract In "Experiment-free exoskeleton assistance via learning in simulation", Luo et al. [1] present an ambitious framework for developing exoskeleton controllers through reinforcement learning exclusively in computer simulation. The authors report that a control policy trained on a small dataset from one subject was directly transferred to physical hardware, reducing human metabolic cost during walking, running, and stair climbing by more than any prior device. If confirmed, this would represent a major breakthrough for the field of wearable robotics and their clinical applications. However, a close examination of the published materials casts doubt on these claims. The reported experimental results violate physiological limits on the relationship between mechanical power and muscle energy use during gait 2,3,4 . The algorithmic claims are surprising and cannot be verified; in contrast with established replicability standards in machine learning 5,6 , executable code has not been made available. We conclude that the goals of this study have not yet been verifiably achieved and make recommendations for avoiding publication errors of this type in the future.

<sec> <title>BACKGROUND</title> Smartwatches are commonly used to estimate energy expenditure, but studies have reported errors of 25% to 70%. The large range in estimation accuracy may be due to numerous factors, such as activity type, use case, and sensor data. We evaluated the differences in energy expenditure estimation among different smartwatches and models to identify the optimal use cases to achieve minimal error. </sec> <sec> <title>OBJECTIVE</title> We systematically evaluated the impact of different sensor inputs on energy expenditure estimation during outdoor activities by comparing a smartwatch operated in an activity-specific mode that incorporated GPS, IMU, and heart rate data; a smartwatch operated in a generic, non-activity-specific, mode that incorporated IMU and heart rate data; and a pre-defined heart rate model. </sec> <sec> <title>METHODS</title> We compared an activity-specific smartwatch, a non-activity specific smartwatch, and a heart rate model to isolate how different sensor inputs impact energy expenditure estimation. Thirty adults (17 men, 13 women; 34 ± 14 years) completed outdoor activities while wearing a portable respirometry system and both smartwatches. Participants performed self-paced walking and running conditions, and followed ecologically relevant audio prompts to elicit real-world walking. Energy expenditure estimates were collected from each smartwatch, and a predefined heart rate model was used to exclude wrist motion. Cumulative energy expenditure from each model was compared with ground-truth respirometry. </sec> <sec> <title>RESULTS</title> The activity-specific smartwatch achieved 13% error during outdoor walking with three times lower error than the non-activity specific smartwatch and heart rate model. During outdoor running, the heart rate model yielded a lower error than both the activity-specific and non-activity specific smartwatches. </sec> <sec> <title>CONCLUSIONS</title> Energy expenditure estimation reached near laboratory-grade performance when both activity-specific information and GPS data were used for monitoring low-intensity activities, while heart-rate alone appeared better suited for high-intensity activities. Meanwhile, estimates from non-activity specific smartwatches should be interpreted cautiously by researchers, clinicians, and users when monitoring physical activity. </sec>

Physical inactivity is the fourth largest cause of global mortality. Health organizations have requested a tool to objectively measure physical activity because many specific and causal relationships between activity and health outcomes are not clearly understood. Existing activity monitors are either unsuitable for large-scale use or have substantial error. We present OpenMetabolics, a biomechanically-informed activity monitor that employs a smartphone in a pants pocket which measures leg motion to estimate energy expenditure. OpenMetabolics uses a data-driven machine learning model to capture the relationship between underlying leg muscle activity and energy expended during common physical activities. OpenMetabolics estimated energy expenditure with 18% cumulative error across all real-world activities, approximately two times lower than existing tools. We developed a pocket motion artifact correction model to accurately monitor energy expenditure when the smartphone is in a pocket of various types of clothing. A week-long, at-home monitoring study highlighted individual and population-level activity patterns across various timescales. We have made the data, code, and smartphone application open source. This accurate and accessible activity monitor could be deployed for large-scale studies with many patient populations to relate activity to health outcomes, inform health policy, and develop interventions.

Community-based locomotor training post-stroke has shown improvements in independent ambulation by increasing dose, intensity, and specificity of walking practice. Robotic ankle exoskeletons hold the potential to facilitate continued rehabilitation at home, but understanding what aspects of the design are most relevant for successful translation to the community presents a challenge. Here, we design a portable rigid ankle exoskeleton to use as a research platform for investigating the effect of assistance on post-stroke gait during overground, community-based walking. We first test our device with stroke survivors and validate its potential for future community use. We then present a systematic method for quantifying power transmission losses at each transmission stage from the battery to the wearer, using data gathered from walking trials with healthy participants. Our evaluation method revealed inefficiencies in power transfer at the interface level, likely resulting from the compliance in the structural components of the system, which motivates future redesign considerations. Overall, our method provides a framework to identify and characterize the components that must be redesigned to lower exoskeleton weight and maximize performance.

Abstract Assistive robotic devices like exoskeletons offer the promise of improving mobility for millions of people. However, developing devices that improve an objective mobility metric is challenging. Human-in-the-loop optimization is a systematic approach for personalizing robotic assistance to maximize a mobility metric that has improved device performance for different metrics and applications. Successfully performing human-in-the-loop optimization requires the experimenter to make many decisions, like selecting the appropriate optimization algorithm, hyperparameters, and convergence criteria. Typically, selecting these experimental settings involves pilot experimentation. We propose an approach that uses a probabilistic surrogate model, mapping assistance parameters to corresponding experimental evaluations of the objective mobility metric, to simulate human-in-the-loop optimization and inform these decisions. In this paper, we form a surrogate model of the metabolic landscape of walking with exoskeleton assistance using an existing experimental dataset. We simulate human-in-the-loop optimization by using a synthetic metabolic landscape model to evaluate the metabolic cost of walking with different assistance parameters, instead of performing an experimental measurement. We perform three simulated scenarios optimizing assistance for an expert subject, a novice subject adapting to the device, and an expert subject with up to 20 assistance parameters. The code and analyses from this work are open-source to promote use by other researchers. Simulation enables direct comparison of optimization settings to inform experimental human-in-the-loop optimization and potentially reduce the resources and time required to develop effective assistive devices.