For prosthesis users, knee units can range from simple devices costing $2000 up to $45,000 for high-end, microprocessor-controlled systems. These higher-end electronic knees provide significant advantages in stability, gait, and metabolic rate compared to their passive or mechanical counterparts. However, the high cost of such systems makes them inaccessible to most amputees. In this study, it was hypothesized that a microprocessor knee could be manufactured for less than $1000, with comparable stability and user experience to a high-end industry standard device. A prototype (E-Knee) was designed with a specific emphasis on stance stability, and was tested during patient gait trials. The gait trials used a repeated measures design to compare three knee devices (a simple passive knee, the prototype E-Knee, and a high-end knee). Ground reaction forces and a functionality questionnaire were used to compare devices. A microprocessor locking test was used to evaluate the prototype’s ability to prevent falls. Building on the LIMBS M3, a passive four-bar polycentric device, the E-Knee added sensing, computing, and controlling capabilities for a material cost of $507. Initial data from a two-subject trial served as proof-of-concept to validate the prototype and found that it improved gait by providing more stability than the M3 and had more gait-pattern similarities to the Ottobock C-Leg than to the M3. Patients reported no perceived differences in stability between the E-Knee and the C-Leg. Patient trials supported that the E-Knee prototype behaved more naturally than the low-end M3 device and had similar ground reaction forces to the C-Leg.

Advancements in computational musculoskeletal biomechanics are constrained by a lack of experimental measurement under real-time physiological loading conditions. This paper presents the design, configuration, capabilities, accuracy, and repeatability of The University of Texas at El Paso Joint Load Simulator (UTJLS) by testing four cadaver knee specimens with 47 real-time tests including heel and toe squat maneuvers with and without musculotendon forces. The UTJLS is a musculoskeletal simulator consisting of two robotic manipulators and eight musculotendon actuators. Sensors include eight tension load cells, two force/torque systems, nine absolute encoders, and eight incremental encoders. A custom control system determines command output for position, force, and hybrid control and collects data at 2000 Hz. Controller configuration performed forward-dynamic control for all knee degrees-of-freedom (DOFs) except knee flexion. Actuator placement and specimen potting techniques uniquely replicate muscle paths. Accuracy and repeatability standard deviations across specimen during squat simulations were equal or less than 8 N and 5 N for musculotendon actuators, 30 N and 13 N for ground reaction forces (GRFs), and 4.4 N·m and 1.9 N·m for ground reaction moments. The UTJLS is the first of its design type. Controller flexibility and physical design support axis constraints to match traditional testing rigs, absolute motion, and synchronous real-time simulation of multiplanar kinematics, GRFs, and musculotendon forces. System DOFs, range of motion, and speed support future testing of faster maneuvers, various joints, and kinetic chains of two connected joints.

Rigid-body knee models have gained popularity thanks to computational speed and ease of setup compared to finite element models—showing exciting potential for clinical patient-specific models in the future. However, Rigid-body studies in general have encountered difficulty in modeling cartilage and especially meniscus material properties, often relying on computationally costly optimization techniques. This paper presents two new methods to alleviate the difficulty—one to define model contact pressure and one to define meniscus internal forces—and is the first to our knowledge to use experimental pressure-strain curves from the literature to simulate cartilage and meniscus behavior in a rigid body model. This paper describes the methodology to derive the proof of concept model and preliminary results from a gait simulation based on ISO 14243-1.