Modelling an above knee prosthesis-a kinematics approach

An attempt has been made to mathematically model a totally mechanical passive type pneumatic damper controlled AK prosthesis for swing control. An Alimco, India made AK prosthesis was taken and was upgraded to a pneumatic damper controlled one. A CAD model of the same was made on ProE software and moment of inertia was found and the data were used for calculating the swing time using kinematics model approach. The prosthesis coupled with an electronic interface was experimented to find the swing time. The experimental results matched the theoretical ones within acceptable limits and it is concluded that kinematics model approach as given by the authors is an effective tool to design passive AK prosthesis and the model can also be used for designing active knee

A CAD Based Dynamic analysis approach to AK prosthesis design

Paper introduces dynamic simulation approach based on computer aided design (CAD), which was applied for development of a passive above-knee (AK) prosthesis to enable a one leg amputated patient with a reasonable size of stump. A locally-fabricated prosthetic knee was modified to develop a pneumatic damper-controlled AK prosthesis. Design studies were carried out to find center of gravity and moment of inertia of the assembly. CAD dynamic analysis was conducted on ProE software and the results were compared to actual swing time found experimentally in the lab. The leg was tested for validation on subjects with positive result

A Depth of Anaesthesia Index from Linear Regression of EEG Parameters. Journal of Clinical Monitoring and Computing

Objective. The field of Anaesthesia has recently witnessed numerous advances both in the drug administration and monitoring of anaesthetic state. This development has further boosted the efforts and interest of researchers in the automation of clinical Anaesthesia. The success in this direction is possible only when assessment of the depth of hypnotic component of anaesthesia is achieved accurately. This paper describes a technique to arrive at a reliable Depth of Hypnosis (DoH) index using electroencephalographic (EEG) parameters. Methods. EEG data from nine patients was recorded and processed to obtain a total of 21 EEG parameters. They were reduced to a set of best five parameters after applying graphical variance analysis which evaluates their power to discriminate between awake and unresponsive states. These five parameters were normalized with respect to awake state and used in a first order equation to give DoH index. Results. The value of computed DoH index varied from 0.37 to 0.58 for different patients during anesthetized state (awake value 1). For a single patient, the maximum variation in the index was observed as ±5% for different epochs at constant dose. Conclusions. A combination of irregularity of EEG waveform in time-domain and band powers in frequency domain best describes the difference between awake and anesthetized states. To characterize these states, a set of optimum EEG parameters exists. These parameters must be normalized to reduce interpatient variability. The calculated graded index may be used to assist the anaesthetist in the operating theatre

EEG Signal Processing for Monitoring Depth of Anesthesia

Among the various functional organ systems within the human body, the central nervous system (CNS) is affected maximum by anesthetic drug. Since electroencephalography (EEG) is a phenomenon of cerebral cortex, this signal is the best indicator of anesthesia. The raw EEG signal recorded from the patient in the operation theatre has many undesirable constituents or artifacts which don't allow it to be used directly for predicting the depth of anesthesia. These artifacts include scalp muscle interferences, presence of power line frequency carrier, electrocardiographic (ECG) and eye-blinking effects. This paper describes the methods used to remove these artifacts and enumerates various parameters of EEG signal which undergo significant changes with anesthetic dose. Data processing of 'clean' EEG signal may be carried out using statistical methods to extract those EEG parameters which discriminate maximum between awake and anesthetized states of human patients

A novel intuitionistic fuzzy approach for tumour/hemorrhage detection in medical images

427-434This study presents a novel method to detect edges that clusters, thresholds, and then detects edges of tumour/ hemorrhageregion using intuitionistic fuzzy set theory. Clustering segments image into several clusters and histogram thresholding eliminatesunwanted clusters that are not related to tumour/hemorrhage region. Finally, image is edge detected, where a clear boundary isobtained. Proposed method performs better than existing edge detection methods

Fuzzy Model for Estimating Induction Dose for General Anesthesia

At present, anesthetist estimates initial anesthetic dose required to produce induction in general anesthesia. This paper proposes a fuzzy model for deciding this dose based on patient data (age, gender, height and weight) and computes the dose by defining IF-THEN rules between age and body surface area. Deviation of predicted initial anesthetic dose from the actual dose given by the anesthetist to patients (9) was found to be within ± 7%

Microcontroller USB interfacing with MATLAB GUI for low cost medical ultrasound scanners

This paper presents an 8051 microcontroller-based control of ultrasound scanner prototype hardware from a host laptop MATLAB GUI. The hardware control of many instruments is carried out by microcontrollers. These microcontrollers are in turn controlled from a GUI residing in a computing machine that is connected over the USB interface. Conventionally such GUIs are developed using ‘C’ language or its variants. But MATLAB GUI is a better tool, when such GUI programs need to do huge image/video processing. However interfacing MATLAB with the microcontroller is a challenging task. Here, MATLAB interfacing through an intermediate MEX ‘C’ language program is presented. This paper outlines the MEX programming methods for achieving the smooth interfacing of microcontrollers with MATLAB GUI