Stroke patients can have spastic paresis of the lower leg, impeding an ankle which hinders gait. A novel orthosis has been developed which counteracts this impediment to the ankle. It is expected that gait training will improve stroke patients' use of the orthosis by increasing their ankle dynamics. Gait training with biofeedback, which is based on physiological signal, has been shown to be effective for stroke patients. The main design requirement for the biofeedback is that it facilitates learning of an increased active range of motion of the ankle. To fulfill this requirement the biofeedback is based on the maximum angle in plantar- and dorsiflexion during the swing phase of the impeded leg. In this research, the biofeedback is validated on healthy participants with an impeded right ankle performing five gait trials. First an unimpeded reference trial was conducted capturing normal gait. After which the participant's ankle was impeded. During the initial impeded trial the participant got accustomed to the impediment during gait. Then two trials with feedback were conducted followed by a retention trial without feedback. During the retention trial the effects the biofeedback has on the ankle dynamics are determined. The outcome measures were chosen to validate whether the biofeedback facilitated learning of an increased active range of motion of the ankle. The outcome measures were the increase of active range of motion of the ankle from the initial impeded trial to the retention trial and the error quotient. The error quotient is a measure showing to what extent the angles making up the active range of motion during a trial were the same as during the reference trial. The active range of motion of the ankle of participants increased (p < 0.001) from the initial impeded trial to the retention trial. Moreover a significant decrease in the error quotient of participants was found between the initial impeded trial and the first feedback trial (p = 0.033), second feedback trial (p = 0.013) and retention trial (p = 0.020). Therefore, the biofeedback facilitated learning of an increased active range of motion of the ankle to participants. Further research is required to determine how to best adapt the biofeedback such that it is suitable for use by stroke patients in daily life.

Purpose – The entropy algorithm is a recently developed statistic for quantifying the complexity of time series data. To date, research of biomechanics and motor control discussed whether entropy algorithms could be used as a convenient tool to identify healthy gait function, to evaluate outcomes of physical therapies and to monitor the progression of disease. Here, we show that Sample Entropy (SaEn) is a sensitive measure for exposing complexity changes in human gait function.

Methods – We analyzed signal complexity changes in electromyography (EMG), ground reaction force (GRF) and joint angle (GA) time series data of asymmetrical step tasks. We used the coarse-grained time series method and the SaEn algorithm, to determine the temporal resolution that contained most complex structures per datatype. Subsequently, we analyzed complexity changes with age and with walking velocity in the selected resolution. We analyzed complexity changes with age, since healthy gait function is known to deteriorate with age. In turn, we analyzed complexity changes with walking velocity, since walking velocity is known to alter gait function. Eighteen young (mean age 23.27 +/- 1.79 years) and nineteen old (mean age 66.37 +/- 5.26 years) subjects were analyzed for an equal number of strides, described by an equal number of samples, to account for the SaEn dataset length bias.

Results – Age increased entropy in EMG signals. Consecutively, age decreased GRF entropy in the medial-lateral (ML) component for short steps and increased entropy for long steps. Lastly, age decreased entropy in GA signals. Furthermore, walking velocity decreased entropy in EMG signals. Consecutively, walking velocity increased GRF entropy in anterior-posterior (AP) and vertical (VE) components and decreased entropy in the medial-lateral (ML) component. Lastly, walking velocity increased entropy in GA signals.

Conclusions – We portrayed that EMG, GRF and GA signals of human gait altered in entropy with walking velocity and with age. Therefore, our results demonstrate the feasibility of SaEn to quantify changes in healthy gait function. Additional research should confirm possible future clinical applications for entropy algorithms.

This report is the result of a graduation project at the TU Delft, Faculty of Industrial Design Engineering. The project was done in collaboration with the Reinier de Graaf Gasthuis. The implantation of a total knee replacement (TKR) is one of the most successful operations in the field of orthopedic surgery. Unfortunately, in 0.6-2.5% of the cases a periprosthetic fracture occurs. Rorabeck type III fractures are located just above the femoral component of a TKR. Due to the bone tissue and the size of the bone it is difficult to get a good grip and make a solid fixation with current fixation techniques. The graduation assignment is stated as follows:‘’Design a new fixation technique that bridges the femoral component of the total knee replacement and the healthy bone above the fracture and enable a solid fixation’’To do so, the project involves current product analysis, product design, prototyping and validating the final design. 

During the analysis phase a list of requirements and demands was created. Combined with the design vision, these are used to generate ideas. From the ideas, three design directions were defined: the smooth surface, clamp cavity and expansion clamp. For each direction the advantages, limitations and unique values were determined. The expansion clamp was expected to be the most promising design direction. It makes use of a fixation device between the longitudinal surfaces by using an expansion clamp. After a second ideation session the most promising concepts have been chosen and presented. To compare the and evaluate the concepts, a Harris profile was created. By analyzing the Harris profile there was chosen to elaborate upon the mechanical expander.

By turning, pushing or screwing a mechanism is activated, panels on the side will spread and put pressure on the implant. There was chosen to use a leaf spring to enable the axpansion. Multiple iteration steps were made to come up with the most ideal design. The features, dimensions, materials and production processes were determined for all parts of the expander. An additional locking plate is designed to fit the expander and bridge the component to the healthy femur above. 

In the last chapter, the final design and its main features are presented. Validation is done by talking to an expert, dr. Verburg, and reflecting on the demands, wishes and design vision that were stated after the analysis. In conclusion, it is likely that the expander with the additional locking plate will provide a more solid fixation of a periprosthetic fracture by integrating the femoral component of a TKR. However, it should be mentioned the design has not been validated and tested on its functionality. The expander can be seen as an addition to the currently used LISS plates. Current designs of locking plates can easily be adjusted to fit the expander. Therefore, it is likely that the product will be accepted by the market. Several recommendations have been defined for further development of the fixation technique based on the validation and risk analysis of the final design.