Methods for Gastrointestinal Endoscopy Quantification: A Focus on Hands and Fingers Kinematics
2. Related work
2.1. Direct Observation
- Scope handling: (1) Exhibits good control of the head and shaft of colonoscope at all times; and (2) Demonstrates ability to use all scope functions (buttons/biopsy channel) whilst maintaining a stable hold on the colonoscope. Minimizes external looping in the shaft of the instrument.
- Tip control: (1) Integrated technique, as the ability to combine tip and torque steering to accurately control the tip of the colonoscope and maneuver the tip in the correct direction; and (2) Individual components: tip steering, torque steering and luminal awareness.
- Air management: Appropriate insufflation and suction of air to minimize over-distension of the bowel while maintaining adequate views.
- Loop management: Uses appropriate techniques (tip and torque steering, withdrawal, position change) to minimize and prevent loop formation.
2.2. Pressure and Force Measurement
- Shergill et al. (2009) evaluated upper-extremity musculoskeletal load . Particularly, they measured right-thumb pinch and downward force and bilateral forearm-muscle activity in both left and right forearms. Essentially, they found high left wrist extensor muscle activity and high right thumb pinch force loads exceeding the threshold levels known to increase the risk of injury . An extension of the previous experiments was performed more recently in 2021, collecting data from thumb pinch force and forearm muscle loads . Differences between male and female endoscopists were observed at the biomechanical level.
- Ref.  A wireless device called the Colonoscopy Force Monitor (CFM) was used to measure the manipulation patterns and force applied by the hands and fingers during the insertion and withdrawal of the tube during a colonoscopy [6,19]. This device was able to capture the over the endoscope: linear force (push/pull or axial) and radial force (torque).  A sensor on the hose of the colonoscope was also used in  for the real-time measurement of the force and torque of the colonoscope applied by the hands and fingers. This work was not focused on training or injury prevention but on supporting the endoscopist by providing information about the force and posture of the distal end of the endoscope to avoid bowel perforation and looping. They refer to previous designs of the endoscope, identifying some issues, particularly that they are bulky and demand two-handed operation.
2.3. Motion Quantification
- In [23,24], right wrist posture and movements were analyzed using a magnetic motion-tracking device. Endoscopists wore a right arm sleeve and glove that were custom-made for this study. The  hypothesis of these studies was that the range of wrist movement (mid, center, extreme, and out) for each wrist DoF (flexion/extension, abduction/adduction, pronation/supination) might decrease as experience in colonoscopy is acquired. It was concluded that fellows spent significantly less time in an extreme range of wrist movements at the end of the study compared to the baseline evaluation.
- Microsoft KinectTM was used in  to measure the technical skills of endoscopists. Seven metrics were analyzed to find discriminative motion patterns between novice and experienced endoscopists: hand distance from the gurney, the number of times the right hand was used to control the small wheel of the colonoscope, the angulation of the elbows, the position of the hands in relation to body posture, the angulation of the body posture in relation to the anus, the mean distance between the hands, and the percentage of time the hands were approximated to each other.
- In Ref. , a motion tracking setup to measure wrist and elbow joint motions is described in . Several wrist and elbow motion metrics are described in this work. For each wrist, the axes of flexion/extension and abduction/adduction motions were analyzed. For each elbow, the axes of flexion/extension and supination/pronation movements were analyzed. For each joint, the number of times the joint entered extreme ranges of motion, as well as the total time spent in extreme ranges of motion.
- In Ref. , flexible wearable sensors that were placed on the dorsum of both hands and the dorsal section of both forearms (2/3 distance from wrist to elbow) were used in [29,30] to compare differences in the movements between novices and experts . Three-dimensional coordinates from the length of the endoscope were taken using a Magnetic Endoscopy Imaging system called ScopeGuide (UPD-3, Olympus, Tokyo, Japan) that includes electromagnetic coils along the length of the endoscope.
2.4. Other Quantifications
3. Hand and Finger Kinematics Quantification
3.1. Hand and Finger Kinematics
3.2. Quantification Technologies
3.2.1. Sensor-Based Technology
3.2.2. Vision-Based Technology
3.3. Smart Gloves
3.3.1. Manus VR Prime II
3.3.2. MoCap Pro
3.4. Camera-Based Systems
4.1. Motion Capture Devices
4.2. The Colonoscopy Process
4.3. Aspects Evaluated of the Two Proposed Capture Systems
- Installation (complicated/time/limited license);
- Calibration (more or less complex and fragile);
- Battery (limitation in measurements/Autonomy);
- Communication (Bluetooth or cable);
- Ease of use (difficulty due to gender or hand size);
- Naturalness (naturalness of use during the handling of tools).
5.1. Set Up Process
5.1.4. Data Treatment
5.2. Lessons Learned with the Devices
5.2.1. Manus VR Prime II Gloves
- Rigid gloves prevent the natural mobility of the hand and therefore are more uncomfortable.
- One size in the purchase, which means having several units.
- Does not allow raw data to be extracted directly for further analysis.
- Does not exchange finger movement data with other measurement devices such as Xsens. To do that, the Prime Xsens version is necessary.
Advantages and Benefits Experienced
- Relatively good battery capacity, allowing 40 min of measurement.
- Wireless enables remote work provided by Bluetooth.
- Support for incorporating external position trackers.
- Data recorded on own encrypted format manus
- Data could be exported as FBX
5.2.2. Stretchsense MoCap Pro Gloves
- Little adaptable to smaller hands, for this specific case, the fingertips are covered, which is inconvenient for the specialist.
- Calibration by individual poses, slower process than with Manus, which is more sequential.
- Difficulty putting on gloves, requiring help from a partner.
- Adduction abduction movements are not appreciated and therefore are not clearly reflected in the hand model.
- The bar graph that shows the movements is very small and does not allow us to appreciate the variation of the data.
Advantages and Benefits Experienced
- Very flexible and therefore comfortable to the touch, allows for more natural measurements.
- Approximately 3 h of use at full capacity.
- Offers alternatives for transmission and/or storage of data captured with SD memory.
- Allows you to extract the captured raw data directly in csv format, quaternion output.
5.2.3. Vero Vicon
- Very delicate calibration regarding the sensitivity of the cameras.
- Assembly time and preparation of the capture scenario of more than 1 h.
- Raw data captured in its own session format and not directly analyzable.
- Requires a prior study of marker placement for later modeling and data interpretation.
- The markers are the essential elements to monitor, and sometimes they do not allow for a natural movement of fingers and hand in certain exercises.
Advantages and Benefits Experienced
- Unlimited battery capacity.
- Very high precision in data capture with a maximum average error of 1 mm.
- Allows you to extract raw data in csv, quaternion output.
- Allows you to extract vrpn, pose data automatically supplied. VRPN clients that are able to make use of velocity and acceleration data can use this information directly from Tracker’s VRPN output rather than having to calculate this on the client side. This is useful if, for example, you want to use Vicon data within a dead reckoning algorithm or other prediction algorithms to estimate poses at a time that may or may not coincide with a Vicon frame.
- None of the analyzed devices offer integration for the analysis of these movements and their correlation with musculoskeletal injuries.
- The two types of gloves studied do not offer a precision range, which calls into question the validity of the data for the scientific study and the quality of the results.
- The markers distributed in this case by hand cause two important problems: (i) the occlusion that causes gaps and, therefore, a loss of information about its trajectory and position (ii) and the difficulty of manipulating the tool, making the maneuver difficult.
- There are very few studies that focus their studies on the analysis of hand modeling due in part to the fact that it is an extremely complex part of the body.
- Make data transmission compatible in both directions so that the precision of the cameras is used to correct a problem they have with occlusions. In addition, the analysis and post-processing of data from both points of view, that of the hands and body posture, would allow for obtaining more significant conclusions.
- Design personalized gloves that are highly adapted to the contour and silhouette of the hand, not interfering and not hindering the fine manipulation of microsensors such as Polhemus Micro Sensors. Therefore, the design of a glove with an array of infrared LEDs is proposed, just as the Vero Vicon calibration stick is installed.
- Modeling of the hand integrated with Deep Learning so that it speeds up and corrects the gaps that are produced by the occlusion of the markers or, failing that, the array of infrared LEDs that integrates the previously proposed glove.
- Standardized data analysis that integrates data types such as quaternion, which are recorded by these devices avoiding intermediate filtering and pre-processing for subsequent reproduction and simulation of mobility. A simulation is already offered by free software programs such as openSim, which is recognized by the scientific community.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Publication||Measurement Method/Devices||Entity||What Is Measured||Participants and Procedures|
| Shah et al., 2002||Observations (in video)||Upper limbs||Manipulation skills of endoscope controls. Endoscope insertion tube manipulation. Depth of insertion||18 endoscopists|
| Battevi et al., 2009||Observation (in video)||Upper limbs||Frequency of technical actions per minute. The awkward posture of the hand continuously moving in a pinch of the endoscopic wheels. For the left arm, the static posture to bear instruments. Fine movements of left-hand fingers. Right elbow flexion–extension movements. Wrist postures in ulnar deviation of the right thumb.||6 endoscopists|
| Shergill et al., 2009|
 Shergill et al., 2021
|Pressure: tactile pad|
Force: surface EMG
Right and left forearm bilateral muscles
|Right thumb pinch force|
Forearms muscle activity
|7 experienced, 9 trainees|
Total: 49 real col.
| Korman et al., 2010|
 Korman et al., 2012
|Pressure: load cells||Endoscope tube||Push/pull force|
Torque: clockwise and counterclockwise
|3 experienced endoscopists|
9 real col.
|12 endoscopists. 2–4 real col.|
| Obstein et al., 2011||Kinematics: electromagnetic|
6D motion sensors
|Endoscope tube||Path length, flex, velocity, acceleration, jerk, tip angulation, angular velocity,|
rotation and curvature of the endoscope. A kinematic score is calculated for
|9 novice endoscopists|
4 experienced ones
A physically simulated col.
| Browne and|
Force: surface EMG
|- Wrist posture (flexion/extension)|
- Muscle activity
|10 novice endoscopists|
5 (physically simulated stomach
| Arnold et al., 2013||Kinematics: motion sensors||Endoscope tube||Longitudinal displacement|
| Svendsen and|
|Body||Level of the left hand below the z-axis. Level of the right hand below the z-axis. Distance between hands. Distance shoulder-hand. Angulation of right elbow. Angulation of left elbow. Angulation of shoulders to the anus||10 experienced|
A physically simulated col.
| Mohankumar et al.,|
 Ratuapli et al., 2015
|Kinematics: magnetic tracker (Polhemus Fastrak)||Wrist||Wrist posture (flexion/extension, abduction/adduction, pronation/supination)|
Wrist motion range (mid, center, extreme, and out) for each wrist DoF
(flexion/extension, abduction/adduction, pronation/supination)
2 VR simulated col.
5 first-year fellows
4 VR simulated col.
| Nerup et al., 2015||Magnetic endoscope imaging||Endoscope tube||Colonoscopy Progression Score (CoPS)|
A map of progression in colonoscopy
|11 novices. 10 experienced|
A physically simulated col.
| Zheng et al., 2017||Force: resistance-strain||Endoscope tube||Axial force: push and pull force|
Torque: clockwise and counterclockwise
| Velden, 2018||Kinematics: IMU trackers (Xsens)|
Webcam and color band
External camera- Kinematics: Vision
End. wheel rotation
|23 body segments: pelvis, L5, L3, T12, T8, neck, head, right and left|
shoulder, upper arm, forearm, hand upper leg, lower leg, foot, and toe.
Translation and torque.
Angle of the two endoscope wheels
Insertion, retroflexion and inspection, duodenal intubation, and retraction
|9 endoscopists (3 beginners and|
26 uppers gastrointestinal
| Holden et al., 2018||Kinematics: electromagnetic|
3D motion sensors
|Hand, arm and forearm (wrist|
and elbow joints)
|The total path length of hands, number of discrete hand motions, wrist motions|
(flexion/extension, abduction/adduction) elbow motions (flexion/extension,
supination/pronation), number of times in extreme ranges of motion.
|22 novice, 8 experienced|
A physically simulated col.
(Wooden bench-top model)
| Levine et al., 2018 |
 Krill et al., 2019
|Kinematics: motion trackers (Biostamp RCTM)||Left and right forearms|
|3D linear accelerations|
A VR simulated col.
|30 novices, 18 experienced|
| Vilmann et al., 2020||Kinematics: motion sensor||Endoscope tube||3D data: travel length, tip progression, chase efficiency, shaft movement|
without tip progression, looping
|12 novices, 12 experienced|
2 physicals simulated col.
| He et al., 2020||Eye gaze tracking (Tobii X2-60)||Eye gaze||Scope disorientation. Eye gaze fixation. Eye gaze saccade||20 novices, 6 experienced|
| Fulton et al., 2021||Kinematics: magnetic tracker (Polhemus Patriot)||Endoscope tube||Translation trajectory and rotation of the endoscope||Physically simulated col.|
Installation Time 1
|Calibration Time |
|Manus VR Prime II||30 min||1 dongle||<1 min||<2 min||<2 min|
|Mocap Pro Gloves||30 min||2 dongles||<1 min||<5 min||<2 min|
|Vero Vicon v2.2||50 min||6 RJ45 wires||90 min 2||<5 min||0 min|
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Otero-González, I.; Caeiro-Rodríguez, M.; Rodriguez-D’Jesus, A. Methods for Gastrointestinal Endoscopy Quantification: A Focus on Hands and Fingers Kinematics. Sensors 2022, 22, 9253. https://doi.org/10.3390/s22239253
Otero-González I, Caeiro-Rodríguez M, Rodriguez-D’Jesus A. Methods for Gastrointestinal Endoscopy Quantification: A Focus on Hands and Fingers Kinematics. Sensors. 2022; 22(23):9253. https://doi.org/10.3390/s22239253Chicago/Turabian Style
Otero-González, Iván, Manuel Caeiro-Rodríguez, and Antonio Rodriguez-D’Jesus. 2022. "Methods for Gastrointestinal Endoscopy Quantification: A Focus on Hands and Fingers Kinematics" Sensors 22, no. 23: 9253. https://doi.org/10.3390/s22239253