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This paper presents a data driven global linear model of steady state walking dynamics. Instantaneous whole body angular momentum is a physics informed aggregate quantity used as a marker for dynamic balance during locomotion. Gait dynamics are often modeled as hybrid and nonlinear. We propose using Koopman Operators to model the gait stability dynamics with a global, linear model. This is achieved by augmenting the whole body angular momentum state variables with learned observables, or basis functions, such that the dynamics look linear in the lifted dimension. Considering that the gait dynamics are periodic, a regularization term that encourages the state transition matrix to be orthonormal is added to the loss term when learning the observables. This forces a periodic model to be learned and prevents the likelihood of unstable poles. A low average MSE was obtained over 2 gait cycles for different population types, each with slightly differing gait dynamics. Furthermore, this linear representation enables the use of linear analysis tools that could have clinical implications for assessing the gait of different patient groups. 

As over 11,000 people turn 65 each day in the U.S., our country, like many others, is facing growing challenges in caring for elderly persons, further exacerbated by a major shortfall of care workers. To address this, we introduce an eldercare robot (E-BAR) capable of lifting a human body, assisting with postural changes/ambulation, and catching a user during a fall, all without the use of any wearable device or harness. Our robot is the first to integrate these 3 tasks, and is capable of lifting the full weight of a human outside of the robot’s base of support (across gaps and obstacles). In developing E-BAR, we interviewed nurses and care professionals and conducted user experience tests with elderly persons. Based on their functional requirements, the design parameters were optimized using a computational model and trade-off analysis. We developed a novel 18-bar linkage to lift a person from a floor to a standing position along a natural trajectory, while providing maximal mechanical advantage at key points. An omnidirectional, nonholonomic drive base, in which the wheels could be oriented to passively maximize floor grip, enabled the robot to resist lateral forces without active compensation. With a minimum width of 38 cm, the robot’s small footprint allowed it to navigate the typical home environment. Four airbags were used to catch and stabilize a user during a fall in ≤ 250 ms. We demonstrate E-BAR’s utility in multiple typical home scenarios, including getting into/out of a bathtub, bending to reach for objects, sit-to-stand transitions, and ambulation. 

It is often challenging to pick suitable data features for learning problems. Sometimes certain regions of the data are harder to learn because they are not well characterized by the selected data features. The challenge is amplified when resources for sensing and computation are limited and time-critical, yet reliable decisions must be made. For example, a robotic system for preventing falls of elderly people needs a real-time fall predictor, with low false positive and false negative rates, using a simple wearable sensor to activate a fall prevention mechanism. Here we present a methodology for assessing the learnability of data based on the Lipschitz quotient.We develop a procedure for determining which regions of the dataset contain adversarial data points, input data that look similar but belong to different target classes. Regardless of the learning model, it will be hard to learn such data. We then present a method for determining which additional feature(s) are most effective in improving the predictability of each of these regions. This is a model-independent data analysis that can be executed before constructing a prediction model through machine learning or other techniques. We demonstrate this method on two synthetic datasets and a dataset of human falls, which uses inertial measurement unit signals. For the fall dataset, we identified two groups of adversarial data points and improved the predictability of each group over the baseline dataset, as assessed by Lipschitz, by using 2 different sets of features. This work offers a valuable tool for assessing data learnability that can be applied to not only fall prediction problems, but also other robotics applications that learn from data. 

Grab bars have been widely used for assisting elderly people with mobility and providing support for daily activities. This work aims to expand the notion of grab bars beyond fixed installations by the use of a mobile robot that can place a handlebar at any point in space, to optimally support postural transitions. A survey of elderly people and care professionals indicated that such a device must be sturdy, providing secure support without sliding or tipping over, yet also have a compact footprint to be maneuverable within confined spaces. Here, we propose a novel two-body robot structure, consisting of two small-footprint mobile bases connected by a four bar linkage where handlebars are mounted. Each base measures only 29.2 cm wide, making the robot likely the slimmest ever developed for mobile postural assistance. Through kinematic analysis, it is shown that the two-body structure can bear the entire weight of a human body, meeting required load bearing specifications as a handlebar. A control plan is proposed that is generalizable to all robots with two nonholonomic mobile bases connected by a coupling mechanism. This consists of a leader-follower scheme, in which the bases are connected by a virtual spring, as well as various enhancements to waypoint tracking and dead reckoning that allow the robot to smoothly and accurately follow a series of waypoints. A prototype robot is constructed, and its performance is validated experimentally. 

Age-related loss of mobility and an increased risk of falling remain major obstacles for older adults to live independently. Many elderly people lack the coordination and strength necessary to perform activities of daily living, such as getting out of bed or stepping into a bathtub. A traditional solution is to install grab bars around the home. For assisting in bathtub transitions, grab bars are fixed to a bathroom wall. However, they are often too far to reach and stably support the user; the installation locations of grab bars are constrained by the room layout and are often suboptimal. In this paper, we present a mobile robot that provides an older adult with a handlebar located anywhere in space - “Handle Anywhere”. The robot consists of an omnidirectional mobile base attached to a repositionable handlebar. We further develop a methodology to optimally place the handle to provide the maximum support for the elderly user while performing common postural changes. A cost function with a trade-off between mechanical advantage and manipulability of the user’s arm was optimized in terms of the location of the handlebar relative to the user. The methodology requires only a sagittal plane video of the elderly user performing the postural change, and thus is rapid, scalable, and uniquely customizable to each user. A proof-of-concept prototype was built, and the optimization algorithm for handle location was validated experimentally. 

In providing physical assistance to elderly people, ensuring cooperative behavior from the elderly persons is a critical requirement. In sit-to-stand assistance, for example, an older adult must lean forward, so that the body mass can shift towards the feet before a caregiver starts lifting the body. An experienced caregiver guides the older adult through verbal communications and physical interactions, so that the older adult may be cooperative throughout the process. This guidance is of paramount importance and is a major challenge in introducing a robotic aid to the eldercare environment. The wide-scope goal of the current work is to develop an in-telligent eldercare robot that can a) monitor the mental state of an older adult, and b) guide the older adult through an assisting procedure so that he/she can be cooperative in being assisted. The current work presents a basic modeling framework for describing a human's physical behaviors reflecting an internal mental state, and an algorithm for estimating the mental state through interactive observations. The sit-to-stand assistance problem is considered for the initial study. A simple Kalman Filter is constructed for estimating the level of cooperativeness in response to applied cues, with a thresholding scheme being used to make judgments on the cooperativeness state.