Compensating Uncertainties in Force Sensing for Robotic-assisted Palpation

Lena Olsson^*
*Correspondence: Lena Olsson, Department of Biomedical Engineering, National Autonomous University of Mexico (UNAM), Mexico City 04510, Mexico, Mexico, Email:

Introduction

The increasing demand for advanced medical technologies, particularly in the field of robotic surgery and diagnostic procedures, has led to significant innovations in robotic-assisted palpation systems. Robotic-assisted palpation refers to the use of robotic mechanisms to simulate the tactile sensation of palpation by human hands, which plays a crucial role in the diagnosis and examination of internal tissues and organs. These systems often combine force sensing technologies with robotic actuation, enabling highly precise and controlled tactile exploration of the human body, providing valuable information for medical professionals during surgery or diagnostics. One of the core challenges in robotic-assisted palpation is compensating for uncertainties in force sensing. Force sensors are critical in determining the amount of pressure exerted by the robotic system during palpation, as this force must be within an optimal range to avoid causing harm to delicate tissues while providing sufficient information for diagnosis. However, the uncertainty in force measurement and the variability of the environment pose significant challenges to the accuracy and reliability of force sensing systems. Factors such as sensor noise, material properties, mechanical compliance and human variability contribute to these uncertainties, making it difficult to ensure consistent and precise force application across different scenarios [1].

Poly Carbonate Urethane (PCU) has been widely used as a material for medical devices due to its excellent mechanical properties, such as high tensile strength, flexibility and biocompatibility. However, despite these advantages, PCU surfaces are not inherently resistant to thrombus formation when exposed to blood. This limitation stems from the polymer’s surface characteristics, which can promote protein adsorption and platelet adhesion, key events that initiate clot formation. Consequently, there has been a concerted effort to develop surface modification strategies that enhance the thromboresistant properties of PCU without compromising its structural integrity [2].

Description

Force sensing is a pivotal component of robotic-assisted palpation systems. These sensors measure the forces exerted by the robotic system as it interacts with the patient’s body, providing critical feedback for the system’s control mechanisms. There are several types of force sensors used in robotic systems, each with its own strengths and limitations. Strain gauges, one of the most commonly used force sensors, operate by detecting the change in resistance that occurs when the sensor is deformed under applied force. While strain gauges offer high accuracy, they can be prone to drift and temperature sensitivity, which can lead to errors over time. Piezoelectric sensors, which generate electrical charge in response to force, are another common choice for robotic palpation. These sensors are known for their high sensitivity and ability to detect dynamic forces, making them suitable for applications requiring real-time force measurements. However, piezoelectric sensors are typically limited in their ability to measure static or constant forces, which makes them less suitable for applications where steady pressure is required. Capacitive sensors, on the other hand, work by measuring changes in capacitance as an object moves closer or further from the sensor and are often used for measuring surface interactions. Though capacitive sensors are less susceptible to mechanical wear, they are sensitive to environmental factors such as humidity and temperature. Optical sensors, which use light to measure force through changes in reflected light intensity, are less common but are valued for their precision and immunity to electrical interference. Despite the different advantages offered by these sensors, all force sensing technologies face challenges in maintaining accuracy in the presence of environmental noise, mechanical variability and the complex interactions between the robotic system and human tissues [3].

The performance of force sensing in robotic-assisted palpation systems can be compromised by various sources of uncertainty. One of the most common sources of error is sensor noise, which can stem from multiple factors including electrical interference, mechanical vibrations and fluctuations in the sensor’s internal components. Noise in sensor readings can obscure true force values, leading to inaccurate measurements that can hinder the system’s ability to provide reliable feedback. Sensor drift, another common issue, refers to the gradual deviation of sensor outputs from their calibrated values over time. This phenomenon can be caused by environmental conditions such as temperature changes, as well as the physical wear of sensor components. Mechanical compliance, or the ability of the robotic system and patient’s tissue to deform under applied pressure, also introduces uncertainty into force measurements. Human tissues, particularly soft tissues, exhibit nonlinear and time-varying stiffness, which complicates the interpretation of force measurements. For example, the stiffness of tissues can vary between patients or even within different areas of the same patient’s body, making it difficult to standardize the amount of pressure required for accurate palpation. Additionally, human variability in the palpation process, such as differences in hand strength, technique and fatigue levels, further contributes to the uncertainty in force measurements. These factors, along with the inherent variability in the mechanical properties of both the robotic system and the tissues being examined, make it challenging to achieve consistently accurate force sensing in robotic-assisted palpation systems [4].

To address the uncertainties in force sensing, various compensation techniques have been developed. One of the most effective strategies is sensor calibration, which involves adjusting the sensor’s output to match a known reference value. This can help correct errors due to drift, non-linearity and other factors that contribute to uncertainty in force measurements. Calibration procedures can be automated through feedback loops that continuously monitor and adjust the sensor’s output, ensuring accurate measurements over time. In addition to calibration, signal filtering techniques are commonly employed to reduce the impact of noise on sensor data. For example, low-pass filters can be used to eliminate high-frequency noise from the signal, while more advanced algorithms, such as Kalman filtering or adaptive filtering, can be used to account for dynamic variations and improve signal accuracy. These filtering methods help smooth out the data, making it easier for the system to interpret the force exerted during palpation [5].

Conclsuion

In conclusion, compensating for uncertainties in force sensing is a critical challenge in the development of robotic-assisted palpation systems. These systems hold significant promise for enhancing the accuracy and precision of medical diagnostics and surgeries, but their effectiveness is heavily dependent on the reliability of force measurements. The sources of uncertainty in force sensing, ranging from sensor noise and drift to mechanical compliance and human variability, present significant hurdles that must be addressed for these systems to function optimally. Various techniques, including sensor calibration, signal filtering, force compensation algorithms and machine learning, offer promising solutions for mitigating these uncertainties.

Acknowledgment

None.

Conflict of Interest

None.

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