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The devices and tools used by medical professionals today certainly appear a lot different than from just a short time ago. In general, medical professionals—and their patients—have become much more accepting of smart, data-driven medical technologies that streamline and improve the accuracy of a treatment process. Wearable therapeutic and drug-delivery devices, robotic surgical systems, and telehealth monitoring devices represent some of the many recent advancements that have changed the medical device industry for the better.
&#nl
However, even with the introduction of these new medical technologies, many of today’s medical professionals are still being trained with primarily analog and antiquated methods. The medical training devices market remains a largely unserved but growing segment within the grander medical device conglomerate. In fact, a recent Zion Market Research1 report is forecasting the medical education market to reach USD 36.20 billion by the end of the year 2020.
&#nl
 
&#nl
Force Exchanges Are Essential in Most Medical Treatment Processes
&#nl
The medical professional’s “sense of touch” is something learned over time. Being shown how to apply manual therapy to a patient, or use a tool for a procedure, is one thing; having methods to quantify these actions will help medical professionals develop their sense of touch faster. One way to accomplish this is through the creation of smart, sensitive training devices that include embedded features to implement higher standards that cultivate better-trained professionals.
&#nl
Integrating force-sensing technologies into common medical training devices can upgrade a simple tool into an insightful, data-driven educational program. The challenge for design engineers of such training devices is to embed force-capturing mechanisms into tight space constraints that simulate a true-to-life medical environment, without adding complexity to the user. This is why selecting the right embedded technology is crucial to developing a successful training device.
&#nl
Weighing Your Force-Sensing Options
&#nl
There are four core force-sensing technologies that are embedded into smart devices: load cells, strain gauges, microelectromechanical systems (MEMs), and printed force-sensitive resistors (FSRs). The design engineer’s choice among these options comes down to design needs, the level of precision required by the application, and their costs.
&#nl
Load cells are likely the most common force-sensing technology used today and are a great option for medical training devices that require a high-level of precision. However, load cells can be challenging for the design engineer to embed into a tight space, due to their bulky size. They also require routine factory re-calibration after repeated use, which can be costly for the end-user.
&#nl
Strain gauges and MEMs devices both have the benefit of being highly precise, while also being much smaller in size than load cells. They can be more challenging from a sensor integration perspective, as both of them often require third-party outsourcing, due to their complex circuitry. MEMs devices in particular also tend to have a more-expensive up-front cost.
&#nl
FSRs are affordable, ultra-thin force sensors consisting of semi-conductive material contained between two pieces of thin polyester. They are passive elements that act as a resistor in an electrical circuit. When unloaded, the sensor has a high resistance that drops when force is applied. When you consider the inverse of resistance (conductance), the conductance response as a function of force is linear within the sensor’s designated force range. An FSR’s thin form factor, combined with its ability to function on simple circuitry, makes it a very attractive option for design engineers.
&#nl
While FSRs can be calibrated with ease, they are not as precise as load cells, strain gauges, or MEMs devices. However—and especially in the case of medical training devices—a high-level of force measuring precision may not always be necessary. In general, FSR technologies are best used in applications that measure relative change of force, rate of change in force, detection of a force threshold to trigger an action, or detection and measurement contact and touch.
&#nl
 
&#nl
&#nl
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Screen Shot 2019-05-06 at 10.36.15 AM.png
&#nl
&#nl
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&#nl
&#nl
&#nl
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&#nl
&#nl
Above: The sensor on the left is an example of a force-sensitive resistor matrix, while the image on the right represents a single-point force-sensitive resistor. Both sensors have flexible form factors and can be customized in different shapes and in different force or pressure ranges. Image courtesy of Tekscan.
&#nl
Smart Medical Training Devices Incorporating FSR Technology
&#nl
When you consider the countless ways force exchanges are executed across the medical field, there are endless applications for capturing force feedback that challenge and develop a medical professional’s skills. The following examples share real-world and conceptual training devices that utilize FSR technology as the key component to gather force-driven data.
&#nl
Example 1: Force-Sensitive Glove to Evaluate Muscle Spasticity
&#nl
The Modified Ashworth Scale is the current hospital standard for measuring muscle stiffness in hospitalized patients, and it is often used to determine whether a patient may require more or less anti-spasticity medication. Physicians apply manual force to a patient’s muscle or joint and assign a zero-to-four number designation—zero meaning no increase in muscle tone, while a four indicates that the affected region is rigid in flexion or extension.
&#nl
However, the Modified Ashworth Scale is an ultimately subjective evaluation method that can vary from physician to physician and even vary in the same physician at different moments of the day.
&#nl
Recently, a group of researchers developed a training glove embedded with a matrix of FSRs to measure the amount of power needed by a physician to move a patient’s limb. The system was programmed to assign a Modified Ashworth Rating based on the force applied by the user. The physician in training would then be graded on their assessment of the patient against the force feedback data acquired by the training glove, thus, instilling a more quantifiable standard to assess muscle spasticity.
&#nl
Example 2: Training Surgical Technique with Haptic Feedback
&#nl
Thanks to recent advancements in surgical technologies, surgeons are learning new operating methods that make them less “hands on.” Many of these surgical systems incorporate automated or robotic technologies that still rely on the surgeon to make their precise actions, even though they typically do not have a way to relay haptic feedback.
&#nl
A research group with the Center for Advanced Surgical and Interventional Technology (CASIT) at UCLA recently developed a surgical training system that included FSRs embedded into the distal end of the robotic grasper. The forces from each sensor were translated to proportional pressures that relayed to a pneumatic balloon-based tactile feedback system that the surgeon could feel with their fingers. Greater grip force would cause the balloons to inflate, which could then prompt the surgeon to adjust the robot’s grip on the fly.
&#nl
Example 3: Training EMTs for Emergency Scenarios
&#nl
When an individual is struggling to breathe and needs CPR, the EMT may unintentionally apply too much or too little force. Because of this, a force-sensitive CPR training device was developed to help train medical professionals administer the right compression force on a patient, at the right cadence.
&#nl
CPR manikins are commonly used to train medical professionals on CPR technique, but many do not include methods to quantify the applied force. To address this limitation, a medical device company developed training pads embedded with FSRs as a method to capture and quantify force feedback while applying CPR.
&#nl
The CPR training pads were connected to a software program and digital monitor, which would gauge both the force output and the frequency of compressions. Software modules were also programmed for different types of patients receiving the CPR. For example, the student evaluation would be on their ability to apply proper CPR to a child or elderly patients versus a full-grown adult.
&#nl
Example 4: Developing Manual Diagnosis Proficiency
&#nl
While every patient is unique, medical professionals are trained to find abnormalities that could be an indication of a serious health issue. This is especially the case in breast palpation assessment, where there’s a heightened risk of missed palpable lesions that are frequently not seen on imaging.
&#nl
Developed with the support of the National Cancer Institute, the MammaCare PAD training platform was developed to teach physicians to perform effective breast palpation examinations. The pad was designed with a matrix of FSRs to locate and relay more than 1000 levels of examination pressure within each square centimeter. A software program was developed for the MammaCare PAD to relay force feedback as the user applied force to the breast model.
&#nl
With the MammaCare PAD, medical professionals in training have instant feedback on their ability to examine their patient manually.
&#nl
Example 5: Perfecting Injection Administration Technique
&#nl
While some patients may manage better than others, everyone has some measure of anxiety when in the presence of needles. With better trained medical professionals, patients can be assured their procedure will be as efficient and as pain free as possible.
&#nl
Model limbs are common training tools for medical professionals practicing their ability to administer injections or draw blood. Certain procedures may require the medical professional to administer needles up to a specific depth within the patient.
&#nl
One medical device manufacturer began work on an injection training model to train medical professionals to perform biopsies. In this design, FSRs were embedded deep within the model’s vinyl skin covering, which were used to detect point force from a needle. A feedback signal—such as an audible noise or a light—would be triggered once the trainee had met the depth threshold, helping to develop their confidence in executing this challenging procedure. (See image at top of page.)
&#nl
Design a Stronger, Better-Prepared Future for the Medical Profession
&#nl
There’s no such thing as the perfect doctor, surgeon, nurse, EMT, or any other position in the medical profession. However, the medical training devices segment is a growing and viable market space that can be served with smart devices designed to elevate standards for how medical professionals learn their craft. Thin, minimally invasive sensing technologies are key to designing a training tool that could save a future life.
&#nl
Reference
&#nl
“Medical Education Market by Type of Training (Cardiothoracic, Neurology, Orthopedic, Oral and Maxillofacial, Pediatric, Radiology, Laboratory) for On-campus, Distance and Online Mode of Education: Global Industry Perspective, Comprehensive Analysis, and Forecast, 2016 - 2022” (2017) Zion Market Research.
&#nl
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&#nl
However, even with the introduction of these new medical technologies, many of today’s medical professionals are still being trained with primarily analog and antiquated methods. The medical training devices market remains a largely unserved but growing segment within the grander medical device conglomerate. In fact, a recent Zion Market Research1 report is forecasting the medical education market to reach USD 36.20 billion by the end of the year 2020.
&#nl
 
&#nl
Force Exchanges Are Essential in Most Medical Treatment Processes
&#nl
The medical professional’s “sense of touch” is something learned over time. Being shown how to apply manual therapy to a patient, or use a tool for a procedure, is one thing; having methods to quantify these actions will help medical professionals develop their sense of touch faster. One way to accomplish this is through the creation of smart, sensitive training devices that include embedded features to implement higher standards that cultivate better-trained professionals.
&#nl
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&#nl
Weighing Your Force-Sensing Options
&#nl
There are four core force-sensing technologies that are embedded into smart devices: load cells, strain gauges, microelectromechanical systems (MEMs), and printed force-sensitive resistors (FSRs). The design engineer’s choice among these options comes down to design needs, the level of precision required by the application, and their costs.
&#nl
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&#nl
Strain gauges and MEMs devices both have the benefit of being highly precise, while also being much smaller in size than load cells. They can be more challenging from a sensor integration perspective, as both of them often require third-party outsourcing, due to their complex circuitry. MEMs devices in particular also tend to have a more-expensive up-front cost.
&#nl
FSRs are affordable, ultra-thin force sensors consisting of semi-conductive material contained between two pieces of thin polyester. They are passive elements that act as a resistor in an electrical circuit. When unloaded, the sensor has a high resistance that drops when force is applied. When you consider the inverse of resistance (conductance), the conductance response as a function of force is linear within the sensor’s designated force range. An FSR’s thin form factor, combined with its ability to function on simple circuitry, makes it a very attractive option for design engineers.
&#nl
While FSRs can be calibrated with ease, they are not as precise as load cells, strain gauges, or MEMs devices. However—and especially in the case of medical training devices—a high-level of force measuring precision may not always be necessary. In general, FSR technologies are best used in applications that measure relative change of force, rate of change in force, detection of a force threshold to trigger an action, or detection and measurement contact and touch.
&#nl
 
&#nl
&#nl
&#nl
Screen Shot 2019-05-06 at 10.36.15 AM.png
&#nl
&#nl
&#nl

&#nl
&#nl
&#nl
&#nl
&#nl
&#nl
Above: The sensor on the left is an example of a force-sensitive resistor matrix, while the image on the right represents a single-point force-sensitive resistor. Both sensors have flexible form factors and can be customized in different shapes and in different force or pressure ranges. Image courtesy of Tekscan.
&#nl
Smart Medical Training Devices Incorporating FSR Technology
&#nl
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&#nl
Example 1: Force-Sensitive Glove to Evaluate Muscle Spasticity
&#nl
The Modified Ashworth Scale is the current hospital standard for measuring muscle stiffness in hospitalized patients, and it is often used to determine whether a patient may require more or less anti-spasticity medication. Physicians apply manual force to a patient’s muscle or joint and assign a zero-to-four number designation—zero meaning no increase in muscle tone, while a four indicates that the affected region is rigid in flexion or extension.
&#nl
However, the Modified Ashworth Scale is an ultimately subjective evaluation method that can vary from physician to physician and even vary in the same physician at different moments of the day.
&#nl
Recently, a group of researchers developed a training glove embedded with a matrix of FSRs to measure the amount of power needed by a physician to move a patient’s limb. The system was programmed to assign a Modified Ashworth Rating based on the force applied by the user. The physician in training would then be graded on their assessment of the patient against the force feedback data acquired by the training glove, thus, instilling a more quantifiable standard to assess muscle spasticity.
&#nl
Example 2: Training Surgical Technique with Haptic Feedback
&#nl
Thanks to recent advancements in surgical technologies, surgeons are learning new operating methods that make them less “hands on.” Many of these surgical systems incorporate automated or robotic technologies that still rely on the surgeon to make their precise actions, even though they typically do not have a way to relay haptic feedback.
&#nl
A research group with the Center for Advanced Surgical and Interventional Technology (CASIT) at UCLA recently developed a surgical training system that included FSRs embedded into the distal end of the robotic grasper. The forces from each sensor were translated to proportional pressures that relayed to a pneumatic balloon-based tactile feedback system that the surgeon could feel with their fingers. Greater grip force would cause the balloons to inflate, which could then prompt the surgeon to adjust the robot’s grip on the fly.
&#nl
Example 3: Training EMTs for Emergency Scenarios
&#nl
When an individual is struggling to breathe and needs CPR, the EMT may unintentionally apply too much or too little force. Because of this, a force-sensitive CPR training device was developed to help train medical professionals administer the right compression force on a patient, at the right cadence.
&#nl
CPR manikins are commonly used to train medical professionals on CPR technique, but many do not include methods to quantify the applied force. To address this limitation, a medical device company developed training pads embedded with FSRs as a method to capture and quantify force feedback while applying CPR.
&#nl
The CPR training pads were connected to a software program and digital monitor, which would gauge both the force output and the frequency of compressions. Software modules were also programmed for different types of patients receiving the CPR. For example, the student evaluation would be on their ability to apply proper CPR to a child or elderly patients versus a full-grown adult.
&#nl
Example 4: Developing Manual Diagnosis Proficiency
&#nl
While every patient is unique, medical professionals are trained to find abnormalities that could be an indication of a serious health issue. This is especially the case in breast palpation assessment, where there’s a heightened risk of missed palpable lesions that are frequently not seen on imaging.
&#nl
Developed with the support of the National Cancer Institute, the MammaCare PAD training platform was developed to teach physicians to perform effective breast palpation examinations. The pad was designed with a matrix of FSRs to locate and relay more than 1000 levels of examination pressure within each square centimeter. A software program was developed for the MammaCare PAD to relay force feedback as the user applied force to the breast model.
&#nl
With the MammaCare PAD, medical professionals in training have instant feedback on their ability to examine their patient manually.
&#nl
Example 5: Perfecting Injection Administration Technique
&#nl
While some patients may manage better than others, everyone has some measure of anxiety when in the presence of needles. With better trained medical professionals, patients can be assured their procedure will be as efficient and as pain free as possible.
&#nl
Model limbs are common training tools for medical professionals practicing their ability to administer injections or draw blood. Certain procedures may require the medical professional to administer needles up to a specific depth within the patient.
&#nl
One medical device manufacturer began work on an injection training model to train medical professionals to perform biopsies. In this design, FSRs were embedded deep within the model’s vinyl skin covering, which were used to detect point force from a needle. A feedback signal—such as an audible noise or a light—would be triggered once the trainee had met the depth threshold, helping to develop their confidence in executing this challenging procedure. (See image at top of page.)
&#nl
Design a Stronger, Better-Prepared Future for the Medical Profession
&#nl
There’s no such thing as the perfect doctor, surgeon, nurse, EMT, or any other position in the medical profession. However, the medical training devices segment is a growing and viable market space that can be served with smart devices designed to elevate standards for how medical professionals learn their craft. Thin, minimally invasive sensing technologies are key to designing a training tool that could save a future life.
&#nl
Reference
&#nl
“Medical Education Market by Type of Training (Cardiothoracic, Neurology, Orthopedic, Oral and Maxillofacial, Pediatric, Radiology, Laboratory) for On-campus, Distance and Online Mode of Education: Global Industry Perspective, Comprehensive Analysis, and Forecast, 2016 - 2022” (2017) Zion Market Research.
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  string(19) "2019-05-09 16:41:33"
  ["create_at"]=>
  string(19) "2019-05-09 16:41:33"
  [19]=>
  string(0) ""
  ["slug"]=>
  string(0) ""
}