Arash Arami

Emotion on FPGA

Implementation of intelligent and bio-inspired algorithms in industrial and real applications is arduous, time consuming and costly; in addition, many aspects of system from high level behavior of algorithm to energy consumption of targeted system must be considered simultaneously in the design process. Advancement of hardware platforms such as DSPs, FPGAs and ASICs in recent years has made it increasingly possible to implement computationally complex intelligent systems; on the other hand, however, the design and testing costs of these systems are high. Reusability and extendibility features of the developed models can decrease the total cost and time-to-market of an intelligent system. In this work, model driven development approach is utilized for implementation of emotional learning as a bio-inspired algorithm for embedded purposes. Recent studies show that emotion is a mechanism for fast decision making in human and other animals, and can be assumed as an expert system. Mathematical models have been developed for describing emotion in mammals from cognitive studies. Here brain emotional based learning intelligent controller (BELBIC), which is based on mammalian middle brain, is designed and implemented on FPGA and the obtained embedded emotional controller (E-BELBIC) is utilized for controlling real laboratorial overhead traveling crane in model-free and embedded manner. Short time-to-market, easy testing and error handling, separating concerns, improving reusability and extendibility of obtained models in similar applications are some benefits of the model driven development methodology.

Real-time embedded emotional controller

Recent studies show that emotion is a mechanism for fast decision-making in human and other animals. Mathematical models have been developed for describing emotion in mammals. These models, similar to other bioinspired models, must be implemented in embedded platforms for industrial and real applications. In this paper, brain emotional learning based intelligent controller, which is based on mammalian middle brain, is designed and implemented on field-programmable gate arrays, and this emotional controller is applied for controlling of laboratorial overhead traveling crane in model-free and embedded manner. The main features of this controller are leaning capability, providing a model-free control algorithm, robustness and the ability to respond swiftly. By designing appropriate stress signals, a designer can implement a proper trade among control objectives.

Instrumented Knee Prosthesis for Force and Kinematics Measurements

In this work, we present the general concept of an instrumented smart knee prosthesis for in-vivo measurement of forces and kinematics. This system can be used for early monitoring of the patient after implantation and prevent possible damage to the prosthesis. The diagnosis of defects can be done by detecting the load imbalance or abnormal forces and kinematics of the prosthetic knee in function. This work is a step towards the fabrication of an instrumented system for monitoring the function of the knee in daily conditions. Studying the constraints of commercially available prostheses, we designed a minimal sensory system and required electronics to be placed in the polyethylene part of prostheses. Three magnetic sensors and a permanent magnet were chosen and configured to measure the prosthetic knee kinematics. Strain gauges were designed to measure the forces applied to the polyethylene insert. Kinematic and force measurements were validated on a mechanical knee simulator by comparing them to different reference systems. Embedded electronics, including the A/D converters and amplifier were designed to acquire and condition the measurements to wirelessly transmit them to an external unit. By considering the necessary power budget for all components, the optimum coil for remote powering was investigated. The necessary rectifier and voltage doubler for remote powering were also designed. This is the first system capable of internally measuring force and kinematics simultaneously. We propose to package the system in the polyethylene part, bringing versatility to the instrumented system developed, as the polyethylene part can be easily modified for different types of prostheses based on the same principle, without changing the prosthesis design.

A Hidden Markov Model of the breaststroke swimming temporal phases using wearable inertial measurement units

The recent advances in wearable inertial sensors opened a new horizon for pervasive measurement of human locomotion even in aquatic environment. In this paper we proposed an automatic approach of detecting the key temporal events of breaststroke swimming as a tentatively explored technique due to the complexity of the stroke. We used two inertial measurement units worn on the right arm and right leg of seven swimmers to capture the kinematics of the breaststroke. The detection of the temporal phases from the inertial signals was undertaken in the framework of a Hidden Markov Model (HMM). Supervised learning of the HMM parameters was achieved using the reference data from manual video analysis by an expert. The outputs of two well-known classifiers on the inertial signals were fused to unfold the input space of the HMM for an enhanced performance. An average correct phase detection of 93.5% for the arm stroke, 94.4% for the leg stroke and the minimum precision of 67 milliseconds in detection of the key events, suggests the accuracy of the method.

Instrumented prosthesis for knee implants monitoring

In this work we present an instrumented smart knee prosthesis for in-vivo measurement of forces and kinematics. Studying the constraints, we designed minimal sensory systems to be placed in the polyethylene part of the prosthesis. The magnetic sensors and a permanent magnet are chosen and configured to measure the relative kinematics of the prosthesis. Moreover, the strain gauges were designed to measure the forces on the polyethylene part. The kinematic and kinetic measurements on a mechanical knee simulator are validated toward reference systems. The supplementary electronics, including the A/D, amplifier, rectifier and voltage doubler are designed. Consequently, by considering the necessary power budget for all the components to be performed, the optimal coils for remote powering is investigated. The system will be packaged in the polyethylene part. Therefore, by the end we will have a smart polyethylene part which can be easily modified for different types of the knee prosthesis without changing the prosthesis design.

Accurate internal–external rotation measurement in total knee prostheses: A magnetic solution

In this work we tackled the problem of accurate measurement of internal-external (IE) rotations in the prosthetic knee. We presented a magnetic measurement system to be implanted in the knee prostheses in order to measure IE without soft tissue artifacts. The measurement system consisted of a permanent magnet attached under the tibial plate of the prosthesis and a combination of magnetic sensors in the polyethylene insert. Two different sensor configurations were designed, and five different angle estimators for measurement of IE angles were defined and tested based on several static and dynamic measurements toward a stereophotogrammetry motion capture system. Also a noise analysis was done to see which estimators are less sensitive to measurement noise. One-sensor configuration provided lower power budget with dynamic RMS error of 0.49° and a noise range of ±0.53°. Two-sensor configuration doubles the power consumption but provided slightly lower dynamic RMS error (0.37°) and a noise range of ±0.42°, and offers the possibility of having redundancy in case of damaged sensor.

A fast model free intelligent controller based on fused emotions: A practical case implementation

In this paper a combination of brain emotional learning based intelligent controllers (BELBICs) is employed to control an unidentified practical overhead crane. The proposed controller is a model free controller and has the capability to deal with multi objective control problems. These properties make BELBIC a powerful controller for unknown complex systems when identification in not cost effective or cannot be performed. The fast emotional learning capability has resulted in good performance even in short procedure (training) time, which is very important in real time model free control. The proposed controller is implemented on a laboratorial overhead crane in a real time model free control task. Two different loads are added to the crane set to simulate uncertainties of an actual crane. To consider main and extra objectives a nonlinear combination of them is used to generate emotional stress signal. Experimental results show that the proposed control system has so rapid and powerful learning capability that can eliminate any need for prior system identification. The results are compared with original sample controller and HFLC-ANFIS. In comparison with ANFIS compensator it has faster compensation of tracking and regulating of the load swings and in rejecting of disturbances. Also, in presence of disturbances, BELBIC performance does not decrease significantly and is slightly better than the others.

A patient-specific model of total knee arthroplasty to estimate patellar strain: A case study

BACKGROUND Inappropriate patellar cut during total knee arthroplasty can lead to patellar complications due to increased bone strain. In this study, we evaluated patellar bone strain of a patient who had a deeper patellar cut than the recommended. METHODS A patient-specific model based on patient preoperative data was created. The model was decoupled into two levels: knee and patella. The knee model predicted kinematics and forces on the patella during squat movement. The patella model used these values to predict bone strain after total knee arthroplasty. Mechanical properties of the patellar bone were identified with micro-finite element modeling testing of cadaveric samples. The model was validated with a robotic knee simulator and postoperative X-rays. For this patient, we compared the deeper patellar cut depth to the recommended one, and evaluated patellar bone volume with octahedral shear strain above 1%. FINDINGS Model predictions were consistent with experimental measurements of the robotic knee simulator and postoperative X-rays. Compared to the recommended cut, the deeper cut increased the critical strain bone volume, but by less than 3% of total patellar volume. INTERPRETATION We thus conclude that the predicted increase in patellar strain should be within an acceptable range, since this patient had no complaints 8 months after surgery. This validated patient-specific model will later be used to address other questions on groups of patients, to eventually improve surgical planning and outcome of total knee arthroplasty.

Accurate Measurement of Concurrent Flexion–Extension and Internal–External Rotations in Smart Knee Prostheses

In this paper, we present a magnetic measurement system for integration into smart knee prostheses to accurately measure the combination of two knee rotations; namely flexion-extension (FE) and internal-external (IE) rotations. This measurement system consists of two permanent magnets inserted into the femoral and tibial parts of the prosthesis and a configuration of anisotropic magneto-resistive sensors placed in its polyethylene part. The sensor configuration was designed according to the sensitivity analysis. Several angle estimators were defined to obtain accurate angle estimations. These estimators ranged from different linear regression models to artificial neural networks. The estimators were trained and tested on several dynamic measurements of rotations of the prosthesis parts in a mechanical knee simulator also monitored using a stereophotogrammetry motion capture system. Considering the best estimators, the errors (mean ± SD) were 0.0° ± 0.9° and 0.2° ± 1.1° for IE and FE angle estimations, respectively. The imposed abduction-adduction (AA) rotations effect was investigated on the estimators in two cases: when the estimators were trained on data without AA (Case 1) and with AA (Case 2). The internal-external angle estimators showed high robustness to the imposed AA. The recorded errors for the best flexion-extension estimator were 0.9° ± 2.7° for Case 1 and 0.3° ± 1.7° for Case 2. The proposed system has thus demonstrated its ability to accurately estimate concurrent flexion-extension and internal-external rotations.

Emotional control of inverted pendulum system: A soft switching from imitative to emotional learning

Model-free control of unidentified systems with unstable equilibriums results in serious problems. In order to surmount these difficulties, firstly an existing model-based controller is used as a mentor for emotional-learning controller. This learning phase prepares the controller to behave like the mentor, while prevents any instability. Next, the controller is softly switched from model based to emotional one, using a FIS1. Also the emotional stress is softly switched from the mentor-imitator output difference to the combination of objectives generated by a FIS which attentionally modulated stresses. For evaluating the proposed model free controller, a laboratorial inverted pendulum2 is employed.