Objectives The aim of this study was to identify physical design elements that contribute to potential falls in patient rooms. Methods An exploratory, physical simulation–based approach was adopted for the study. Twenty-seven subjects, older than 70 years (11 male and 16 female subjects), conducted scripted tasks in a mockup of a patient bathroom and clinician zone. Activities were captured using motion-capture technology and video recording. After biomechanical data processing, video clips associated with potential fall moments were extracted and then examined and coded by a group of registered nurses and health care designers. Exploratory analyses of the coded data were conducted followed by a series of multivariate analyses using regression models. Results In multivariate models with all personal, environmental, and postural variables, only the postural variables demonstrated statistical significance—turning, grabbing, pushing, and pulling in the bathroom and pushing and pulling in the clinician zone. The physical elements/attributes associated with the offending postures include bathroom configuration, intravenous pole, door, toilet seat height, flush, grab bars, over-bed table, and patient chair. Conclusions Postural changes, during interactions with the physical environment, constitute the source of most fall events. Physical design must include simultaneous examination of postural changes in day-to-day activities in patient rooms and bathrooms. Among discussed testable recommendations in the article, the followings design strategies should be considered: (a) designing bathrooms to reduce turning as much as possible and (b) designing to avoid motions that involve 2 or more of the offending postures, such as turning and grabbing or grabbing and pulling, and so on.

In recent years, there has been a steep rise in the quality of prostheses for patients with upper limb amputations. Researchers have begun to identify methods of making prosthetic hands both functional and cosmetically appealing, in contrast to past designs. Many improvements have occurred because of novel design strategies, such as the use of underactuated mechanisms, which allow for more degrees of freedom (DOF) or help reduce the weight of the prosthesis. The increase in functionality is also due in large part to advancements in control strategies for prosthetic hands. One common control method, using electromyographic (EMG) signals generated by muscle contractions, has allowed for an increase in the DOF of hand designs and a larger number of available grip patterns with little added complexity for the wearer. Another recent improvement in prosthetic hand design instead employs electroneurographic (ENG) signals, requiring an interface directly with the peripheral nervous system (PNS) or the central nervous system (CNS). Despite the recent progress in design and control strategies, however, prosthetic hands are still far more limited than the actual human hand. This review outlines the recent progress in the development of electrode-based prosthetic hands, detailing advancements in the areas of design, sensory feedback, and control through EMG and ENG signals (with a particular focus on interfaces with the PNS). The potential benefits and limitations of both control strategies, in terms of signal classification, invasiveness, and sensory feedback, are discussed. Finally, a brief overview of interfaces with the CNS is provided, and potential future developments for prosthetic hand design are discussed.

A smart choice of contact forces between robotic grasping devices and objects is important for achieving a balanced grasp. Too little applied force may cause an object to slip or be dropped, and too much applied force may cause damage to delicate objects. Prior methods of grasping force optimization in literature have mostly assumed grasp only at the fingertips but have rarely considered how the whole hand grasps more common to anthropomorphic hands affect the optimization of grasping forces. Further, although numerical examples of grasping force optimization methods are routinely provided, it is often difficult to compare the performance of separate methods when they are evaluated using different parameters, such as the type of grasping device, the object grasped, and the contact model, among other factors. This paper presents three optimization approaches (linear, nonlinear, and nonlinear with linear matrix inequality (LMI) friction constraints) which are compared for an anthropomorphic hand. Numerical examples are provided for three types of grasp commonly performed by the human hand (cylindrical grasp, tip grasp, and tripod grasp) using both soft finger contact and point contact with friction models. Contact points between the hand and the object are predetermined. Results are compared based on their accuracy, computational efficiency, and other various benefits and drawbacks unique to each method. Future work will extend the problem of grasping force optimization to include consideration for variable forces and object manipulation.

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The human posture prediction model is one of the most important and fundamental components in digital human models. The direct optimization-based method has recently gained more attention due to its ability to give greater insights, compared to other approaches, as how and why humans assume a certain pose. However, one longstanding problem of this method is how to determine the cost function weights in the optimization formulation. This paper presents an alternative formulation based on our previous inverse optimization approach. The cost function contains two components. The first is the weighted summation of the difference between experimental joint angles and neutral posture, and the second is the weighted summation of the difference between predicted joint angles and the neutral posture. The final objective function is then the difference of these two components. Constraints include (1) normalized weights within limits; (2) an inner optimization problem to solve for the joint angles, where joint displacement is the objective function; (3) the end-effector reaches the target point; and (4) the joint angles are within their limits. Furthermore, weight limits and linear weight constraints determined through observation are implemented. A 24 degree of freedom (DOF) human upper body model is used to study the formulation. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected and a total of 18 target points are designed for this experiment. The results show that using the new objective function in this alternative formulation can greatly improve the accuracy of the predicted posture.