Toward Personalized Orthopedic Care: Validation of a Smart Knee Brace.

Introduction: Wearable technology offers a promising solution to advance current rehabilitation strategies for post-operative orthopedic care. The aim of this study was to determine the level of agreement and concurrent validity of a smart knee brace compared to the gold standard measurement system GAITRite® for assessing lower limb gait parameters. Methods: Thirty-four healthy participants were fitted with the smart knee brace (Digital Knee®) on their dominant limb. Gait parameters (stride length, stride time, and gait velocity) were measured simultaneously using the Digital Knee® and the GAITRite® electronic walkway. Two walks were performed at a comfortable speed and two at a fast-walking speed. Results: At a comfortable walking speed, stride time was moderately valid (ICC2,1 = 0.66 s), and stride length and gait velocity demonstrated poor validity (ICC2,1 = 0.29; ICC2,1 = 0.41). All gait parameters demonstrated poor validity at a fast-walking speed (ICC2,1 = -0.16 to -0.01). Bias ranged from -0.08 to 0.28, with more clinically acceptable percentage errors at a comfortable walking speed (14.1-30%) versus at a fast-walking speed (26.4-42.6%). Gait velocity and stride length had substantially higher biases in the fast-walking speed compared to the comfortable walking speed (0.28 ± 0.39 m s-1 vs. 0.02 ± 0.21 m s-1; 0.15 ± 0.23 m vs. -0.04 ± 0.17 m). Limits of agreement were considered narrower for stride time compared to stride length and gait velocity. Conclusion: The Digital Knee® is a promising approach to improving post-operative rehabilitation outcomes in patients with osteoarthritis. The Digital Knee® demonstrated good agreement and moderate concurrent validity for measuring gait metrics at a comfortable walking speed. These findings highlight the opportunity of the wearable sensor as an intervention for post-operative orthopedic care. This was a laboratory-based study; thus, further research is required to validate the wearable sensor in real-world contexts and in patients with knee pathologies. Further, refinement of the algorithm for measuring gait metrics at slow- and fast-walking speed with the Digital Knee® is warranted.

Review of functional materials for potential use as wearable infection sensors in limb prostheses.

The fundamental goal of prosthesis is to achieve optimal levels of performance and enhance the quality of life of amputees. Socket type prostheses have been widely employed despite their known drawbacks. More recently, the advent of osseointegrated prostheses have demonstrated potential to be a better alternative to socket prosthesis eliminating most of the drawbacks of the latter. However, both socket and osseointegrated limb prostheses are prone to superficial infections during use. Infection prone skin lesions from frictional rubbing of the socket against the soft tissue are a known problem of socket type prosthesis. Osseointegration, on the other hand, results in an open wound at the implant-stump interface. The integration of infection sensors in prostheses to detect and prevent infections is proposed to enhance quality of life of amputees. Pathogenic volatiles having been identified to be a potent stimulus, this paper reviews the current techniques in the field of infection sensing, specifically focusing on identifying portable and flexible sensors with potential to be integrated into prosthesis designs. Various sensor architectures including but not limited to sensors fabricated from conducting polymers, carbon polymer composites, metal oxide semiconductors, metal organic frameworks, hydrogels and synthetic oligomers are reviewed. The challenges and their potential integration pathways that can enhance the possibilities of integrating these sensors into prosthesis designs are analysed.

Experimental Investigation into the Dynamics of a Radially Contracting Actuator with Embedded Sensing Capability.

Dynamics, control, and sensing are still challenges for pneumatically actuated soft actuators. We consider feasible solutions based on a radially contracting actuator to overcome these challenges. The radially contracting actuator was inspired by the movement of the stomach wall. It was capable of achieving radial contraction by inflating its circular air chamber. A quasi-static model that relates the pressure with the deformed wall of the air chamber was proposed and validated. In this article, we conduct a thorough experimental investigation into the contracting dynamics of the actuator with embedded sensing capability. We analyze the kinematics of the actuator at its rest and pressurization states focusing on the midpoint of the deformed wall. The actuator dynamics is characterized under the square wave pressure input by two variables that are the pressure in the air chamber and the trajectories of the midpoint. To achieve the desired contraction, we construct a feed-forward control based on the quasi-static model. It proves that the actuator is capable of tracking a prescribed triangular wave displacement of the midpoint with small deviations. A custom-made soft sensor is integrated into the actuator, which brings in the embedded sensing capability without affecting the actuator compliance. The resistance changes of the sensor versus the controlled contraction are examined, which are used to indicate the amount of radial contraction. The experimental investigation provides a foundation for the closed-loop control and practical applications of the radially contracting actuator developed.

A Learning Scheme for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions.

Electromyography (EMG) based interfaces are the most common solutions for the control of robotic, orthotic, prosthetic, assistive, and rehabilitation devices, translating myoelectric activations into meaningful actions. Over the last years, a lot of emphasis has been put into the EMG based decoding of human intention, but very few studies have been carried out focusing on the continuous decoding of human motion. In this work, we present a learning scheme for the EMG based decoding of object motions in dexterous, in-hand manipulation tasks. We also study the contribution of different muscles while performing these tasks and the effect of the gender and hand size in the overall decoding accuracy. To do that, we use EMG signals derived from 16 muscle sites (8 on the hand and 8 on the forearm) from 11 different subjects and an optical motion capture system that records the object motion. The object motion decoding is formulated as a regression problem using the Random Forests methodology. Regarding feature selection, we use the following time-domain features: root mean square, waveform length and zero crossings. A 10-fold cross validation procedure is used for model assessment purposes and the feature variable importance values are calculated for each feature. This study shows that subject specific, hand specific, and object specific decoding models offer better decoding accuracy that the generic models.

On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions.

The field of Brain Machine Interfaces (BMI) has attracted an increased interest due to its multiple applications in the health and entertainment domains. A BMI enables a direct interface between the brain and machines and is capable of translating neuronal information into meaningful actions (e.g., Electromyography based control of a prosthetic hand). One of the biggest challenges in developing a surface Electromyography (sEMG) based interface is the selection of the right muscles for the execution of a desired task. In this work, we investigate optimal muscle selections for sEMG based decoding of dexterous in-hand manipulation motions. To do that, we use EMG signals derived from 14 muscle sites of interest (7 on the hand and 7 on the forearm) and an optical motion capture system that records the object motion. The regression problem is formulated using the Random Forests methodology that is based on decision trees. Regarding features selection, we use the following time-domain features: root mean square, waveform length and zero crossings. A 5-fold cross validation procedure is used for model assessment purposes and the importance values are calculated for each feature. This pilot study shows that the muscles of the hand contribute more than the muscles of the forearm to the execution of inhand manipulation tasks and that the myoelectric activations of the hand muscles provide better estimation accuracies for the decoding of manipulation motions. These outcomes suggest that the loss of the hand muscles in certain amputations limits the amputees' ability to perform a dexterous, EMG based control of a prosthesis in manipulation tasks. The results discussed can also be used for improving the efficiency and intuitiveness of EMG based interfaces for healthy subjects.

Profiling of headspace volatiles from Escherichia coli cultures using silicone-based sorptive media and thermal desorption GC-MS.

Headspace sorptive extraction technique using silicone based sorptive media coated stir bars is used for the first time here to extract, identify, and quantify heavy volatile organic compounds present in Escherichia coli culture headspace. Detection of infection presence is largely accomplished in laboratories through physical sampling and subsequent growth of cultures for biochemical testing. The use of volatile biomarkers released from pathogens as indicators for pathogenic presence can vastly reduce the time needed whilst improving the success rates for infection detection. To validate this, by using a contactless headspace sorptive extraction technique, the volatile compounds released from E. coli, grown in vitro, have been extracted and identified. Two different sorptive media for extracting these headspace volatiles were compared in this study and the identified volatiles were quantified. The large phase volume and wider retention of this sorptive technique compared to traditional sampling approach enabled preconcentration and collection of wider range of volatiles towards developing an extensive database of such heavy volatiles associated with E. coli. This supplements the existing data of potential bacterial markers and use of internal standards in these tests allows semi-quantitative estimation of these compounds towards the development and optimization of novel pathogen sensing devices.

Soft-stop knee brace for rehabilitation from ligament injuries: Design and pilot trial.

Ligaments within the human knee are commonly torn or injured as a result of sports that involve sharp direction changes, pivoting and landing. For this reason, athletes are often side-lined from their respective sports for up to 18 months after injury to rehabilitate. As part of the rehabilitation process, knee braces are often prescribed in an attempt to decrease the recovery period of the injured individual by restraining and minimizing the motion at the knee. However, the true efficacy of knee bracing is yet to be fully evaluated as studies show mixed results to whether braces actually decrease the rehabilitation period for patients. This paper describes the design and pilot trials of a simple but novel knee brace design that aims to actively aid the rehabilitation of the knee from ligament injuries, primarily the anterior cruciate ligament (ACL). The newly developed knee brace uses an angle locking mechanism with dampers to control both the range of motion of the knee joint and the resistance applied to the knee joint at the limits of extension. Using finite element analysis, these dampers were designed help build muscle strength during everyday use of the knee brace and to reduce the 'jarring effect' which causes significant pain and risk to patients using current knee brace designs. Through providing these features, this new knee brace design has the potential to help improve the extent and speed of recovery for ACL impaired patients.