Owing to the multi-factorial nature of fatigue, leveraging context to effectively monitor and intervene with fatigue symptoms presents a significant challenge. This paper aimed to understand how to incorporate context into designing mHealth systems for fatigue management. We conducted a two-week field study with 20 fatigue-vulnerable individuals using an activity-tracking sensor and self-reporting. We conducted data-prompted interviews to explore phenomena about participants’ fatigue experiences. Findings show a heterogeneous relationship between context and fatigue, which can be attributed to the phenomena that: (1) participants were influenced by multiple fatigue-inducing factors for different durations; (2) broad contexts moderated participants’ perceptions and coping strategies in response to local contexts; (3) the predictability and repetition of activities influenced participants’ fatigue perception and coping strategies. We propose a context-informed layer-based framework integrating these phenomena and discuss implications for designing fatigue management tools informed by our framework.

In previous studies, researchers have successfully measured walking in healthy able-bodied humans to create safe control strategies for lower body assistive devices. measurements used to establish design requirements often come from testing and evaluation that takes place in laboratory settings during steady-state tasks, where participants often select movement strategies that minimize the cost of transport. However, human walking in these conditions does not neces-sarily represent the natural behavior of an individual in the real world. In this work, we conducted a study to characterize human walking in the real world. We combined week-scale free-living measurements of gait with in-lab data collection to: 1) quantify the proportion of steady-state walking in a population of healthy able-bodied adults, and 2) evaluate whether this population favors the selection of a range of walking speeds that minimize their cost of transport in the real world. We found that the majority of walking bouts contain mostly transient walking, suggesting that researchers should complement steady-state characterization with non-steady-state tasks. We also found that the most often used steady-state walking speeds for all participants were higher than the range that minimizes cost of transport, suggesting that individuals are influenced by more than energy economy when moving in the real world. Thus, when developing control strategies for these devices, researchers should consider a variety of optimization objectives to adapt for the multifarious situations of daily life.

In this paper, we investigated how the stiffness and damping properties of soft robotic feet affect the stability and energetic economy of bipedal robotic walking. To this end, we manufactured four different spherical feet from the following materials: hollow rubber, Sorbothane, Norsorex, and Neoprene. The materials were specifically chosen to cover a wide range of stiffness and damping values. The impact response of each design was first characterized in a drop test rig. We then evaluated the performance of each foot in an extensive series of walking experiments on the planar bipedal robot RAM one. Our results showed that, at low speeds, the feet with lower damping had a smaller energy cost of walking, possibly due to greater return of mechanical energy at lift-off. However, at speeds above 0.5m\s, the feet with lower damping started to exhibit a bouncing behaviour which led to higher walking instability and increased the energy cost of walking. Additionally, we found the feet with lower stiffness to be more economical across all walking speeds. Our results provide insight into the role of foot properties in bipedal walking and may help with the design of walking robots.

Tracking of uncontrollable agents in a controlled environment is an important research question for the coordination of controllable and uncontrollable agents and bio-inspired multi-agent control. This paper presents a framework that approaches the multiagent tracking problem from a localization perspective, utilizing a combination of wearable sensors and stationary cameras. Specifically, this framework was applied to localize uncontrollable biological agents (dolphins) in a well defined environment. The biological agents were outfitted with wearable sensors (IMU, speed, depth) and were free to move in their three dimensional habitat. The dynamic data collected by the wearable sensors was supplemented with image data collected using a pair of cameras mounted above the habitat. The framework presented in this paper combines data from these sensor streams to calculate an accurate estimate of the animal's location during extended periods of free movement. The associations between camera detections and tagged agents are handled using a particle filter embedded with a fuzzy observation concept. The platform is readily implementable in similar water / land environments, and is able to handle nonlinear agent dynamics, non-Gaussian noise, and sparse camera observations while maintaining robust agent localization and tracking.

Accurate biological agent localization offers the opportunity for both researchers and institutions to gain new knowledge about individual and group behaviors of biosystems. This paper presents a sensor-fusion approach for tracking biological agents, combining the data from automated video logging with magnetic, angular rate, and gravity (MARG) and inertial measurement unit (IMU) data, with professionally managed dolphins as the representative example. Our method of video logging allows for accurate and automated dolphin location detection using a combination of Laplacian of Gaussian (LoG) and multi-orientation elliptical blob detection. These data are combined with MARG/IMU measurements to generate a localization estimate through a series of drift-correcting Kalman and gradient-descent filters, finalized with Incremental Smoothing and Mapping (iSAM2) pose-graph localization.