Monocular Vision- and IMU-Based System for Prosthesis Pose Estimation During Total Hip Replacement Surgery

Inertial Sensors for Hip Arthroplasty Rehabilitation: A Scoping Review

The objective of this scoping review is to characterize the current panorama of inertia sensors for the rehabilitation of hip arthroplasty. In this context, the most widely used sensors are IMUs, which combine accelerometers and gyroscopes to measure acceleration and angular velocity in three axes. We found that data collected by the IMU sensors are used to analyze and detect any deviation from the normal to measure the position and movement of the hip joint. The main functions of inertial sensors are to measure various aspects of training, such as speed, acceleration, and body orientation. The reviewers extracted the most relevant articles published between 2010 and 2023 in the ACM Digital Library, PubMed, ScienceDirect, Scopus, and Web of Science. In this scoping review, the PRISMA-ScR checklist was used, and a Cohen's kappa coefficient of 0.4866 was applied, implying moderate agreement between reviewers; 23 primary studies were extracted from a total of 681. In the future, it will be an excellent challenge for experts in inertial sensors with medical applications to provide access codes for other researchers, which will be one of the most critical trends in the advancement of applications of portable inertial sensors for biomechanics.

HUMAN DETECTION AND MOTION RECOVERY BASED ON MONOCULAR VISION

ABSTRACT Objective: Provides interactive games and human animation real motion data and technical options. Therefore, how to complete the position, attitude detection, and motion recovery under monocular vision has become an important research direction. Methods: This paper improves the part-based human detection algorithm and uses the AdaBoost multi-instance learning algorithm to train the part detector. Results: The results show that obtaining blood pressure waveform based on monocular vision pulse wave is feasible and has generalization. Conclusions: The results show the feasibility and accuracy of the gait motion detection, motion recovery and analysis system for human lower limbs based on monocular vision. Level of evidence II; Therapeutic studies - investigation of treatment results.

CMOS Image Sensor Design and Image Processing Algorithm Implementation for Total Hip Arthroplasty Surgery

As the average age of the population goes higher, the people undergo hip arthroplasty surgery to replace their stiff and painful damaged hip joints. To reduce the risk factor after the surgery we developed a visual aided system in previous works. In order to solve the integration problems of commercial camera with other sensors into the femoral head and to minimize the area and power consumption, in this paper we propose a CMOS image sensor of resolution 200 × 200 specially designed for the application in which each individual pixel measures around 15 μm × 15 μm in size and the image sensor chip size measures about 3.5 mm × 3.5 mm. The proposed sensor is simulated with the input current variations from 2 pA to 100 pA for the individual pixels and the corresponding measurements for each pixel range from 2 mV to 855 mV. Besides, we put forward a new method of pattern detection and recognition in the blood-covered situation, which provides an accurate segmentation of patterns from the blood. All the detected patterns are recognized by generating its right 9-bit binary ID required for the pose estimation calculation. Furthermore, to reduce system power consumption, we implement algorithms on FPGA to process the image data pixel by pixel and transmit it directly to the computer for post-processing. Experimental results show that the pattern detection rate goes as high as 99%, which is 5% better in accuracy compared to the top hat algorithm. The power consumption of the system is 213 mW, which is a 70% decrease compared to our previous work.

An Implantable Wireless Inductive Sensor System Designed to Monitor Prosthesis Motion in Total Joint Replacement Surgery

Currently, the most common method for detecting prosthetic implant loosening is imaging. Unfortunately, imaging methods are imprecise in detecting the early signs of implant loosening. This paper describes a new wireless inductive proximity sensor system for detecting early implant loosening. The loosening of the implant is accurately detected by analyzing the electromagnetic field generated by the passive sensors located around the implant. The sensor system was modeled and simulated using COMSOL, and then tested experimentally. The inductive proximity sensor and the metallic implant form a coupled circuit is tuned to oscillate at a designed frequency. The circuit's integrated controller measures and records specific sensor's parameters such as resistance and inductance of the sensor that are directly related to the distance between the sensor system and the implant. A prototype has been developed and the results show that the designed proximity sensor is capable of measuring the loosening of the hip implant at [Formula: see text]m resolution at distances of less than [Formula: see text], and of [Formula: see text]m resolution at a distance of [Formula: see text]. Furthermore, there is a good correlation between the simulated and experimental results.

Augmented marker tracking for peri-acetabular osteotomy surgery

OBJECTIVE

To develop a hybrid augmented marker-based navigation system for acetabular reorientation during peri-acetabular osteotomy (PAO).

METHODS

The system consists of a tracking unit attached to the patient's pelvis, augmented marker attached to the acetabular fragment and a host computer to do all the computations and visualization. The augmented marker is comprised of an external planar Aruco marker facing toward the tracking unit and an internal inertial measurement unit (IMU) to measure its orientation. The orientation output from the IMU is sent to the host computer. The tracking unit streams a live video of the augmented marker to the host computer, where the planar marker is detected and its pose is estimated. A Kalman filter-based sensor fusion combines the output from marker tracking and the IMU. We validated the proposed system using a plastic bone study and a cadaver study. Every time, we compared the inclination and anteversion values measured by the proposed system to those from a previously developed optical tracking-based navigation system.

RESULTS

Mean absolute differences for inclination and anteversion were 1.34 ([Formula: see text]) and 1.21 ([Formula: see text])[Formula: see text], respectively, for the cadaver study. Mean absolute differences were 1.63 ([Formula: see text]) and 1.55 ([Formula: see text])[Formula: see text] for inclination and anteversion for the plastic bone study. In both validation studies, very strong correlations were observed.

CONCLUSION

We successfully demonstrated the feasibility of our system to measure the acetabular orientation during PAO.