Towards Haptic Performance Analysis Using K-Metrics

Hall, Richard J.; Rathod, Hemang; Maiorca, Mauro; Ioannou, Ioanna; Kazmierczak, Edmund; O’Leary, Stephen; Harris, Peter

doi:10.1007/978-3-540-87883-4_6

Cited by 10 publications

(9 citation statements)

References 13 publications

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“…Further analysis was done using a second piece of internally developed software that uses a cosine (k-cos) function to detect changes in direction in order to identify strokes. 22 This method of analysis parallels the analysis of Ioannou et al 13 Using this k-cos function, a stroke is determined based on directional change or the curvature within a frame of data points. 13 A frame rate-independent low pass smoothing filter was used to eliminate vibrations from the drill, hand tremor, and experimental error.…”

Section: Methodsmentioning

confidence: 90%

Hand Motion Analysis Illustrates Differences When Drilling Cadaveric and Printed Temporal Bone

Hochman

Pisa

Kazmerik

et al. 2021

Ann Otol Rhinol Laryngol

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Objective: Temporal bone simulation is now commonly used to augment cadaveric education. Assessment of these tools is ongoing, with haptic modeling illustrating dissimilar motion patterns compared to cadaveric opportunities. This has the potential to result in maladaptive skill development. It is hypothesized that trainee drill motion patterns during printed model dissection may likewise demonstrate dissimilar hand motion patterns. Methods: Resident surgeons dissected 3D-printed temporal bones generated from microCT data and cadaveric simulations. A magnetic position tracking system (TrakSTAR Ascension, Yarraville, Australia) captured drill position and orientation. Skill assessment included cortical mastoidectomy, thinning procedures (sigmoid sinus, dural plate, posterior canal wall) and facial recess development. Dissection was performed by 8 trainees (n = 5 < PGY3 > n = 3) using k-cos metrics to analyze drill strokes within position recordings. K-cos metrics define strokes by change in direction, providing metrics for stroke duration, curvature, and length. Results: T-tests between models showed no significant difference in drill stroke frequency (cadaveric = 1.36/s, printed = 1.50/s, P < .40) but demonstrate significantly shorter duration (cadaveric = 0.37 s, printed = 0.16 s, P < .01) and a higher percentage of curved strokes (cadaveric = 31, printed = 67, P < .01) employed in printed bone dissection. Junior staff used a higher number of short strokes (junior = 0.54, senior = 0.38, P < .01) and higher percentage of curved strokes (junior = 35%, senior = 21%, P < .01). Conclusions: Significant differences in hand motions were present between simulations, however the significance is unclear. This may indicate that printed bone is not best positioned to be the principal training schema.

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Section: Methodsmentioning

confidence: 90%

Hand Motion Analysis Illustrates Differences When Drilling Cadaveric and Printed Temporal Bone

Hochman

Pisa

Kazmerik

et al. 2021

Ann Otol Rhinol Laryngol

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“…To facilitate meaningful motion analysis, drilling trajectories were segmented into individual strokes using a kcos-based algorithm adapted from Hall et al 15 A stroke was defined as a continuous drilling motion without a significant change in direction. In cadaveric data, the drilling periods were identified manually using video recordings.…”

Section: Methodsmentioning

confidence: 99%

The effect of fidelity: How expert behavior changes in a virtual reality environment

et al. 2014

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Slight differences in drill interaction between a virtual environment and the real world can have measurable effects on surgical technique, particularly in terms of stroke length, duration, and curvature. It is important to understand these effects when designing and implementing surgical training programs based on VR simulation--and when improving the fidelity of VR simulators to facilitate use of a similar technique in both real and simulated situations.

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“…A stroke is a collection of low-level data points segmented using an adapted k-cos algorithm based on [Hall et al 2008]. Only the drill motions that result in material removal are considered to be part of a stroke.…”

Section: Stroke Metricsmentioning

confidence: 99%

“…at each time interval of these segmented runs to train an Emerging Patterns (EP) classifier ] to predict the current surgical stage. Second, we aggregate the above low level data into surgical strokes using an online k-cos method inspired by [Hall et al 2008] and calculate high level metrics for each stroke (such as stroke duration, stroke speed, distance to anatomical structures etc.). These stroke metrics are used to train a separate Emerging Patterns classifier for each stage of the procedure, to capture the differences in technique between expert and trainee groups and propose real-time feedback to improve performance.…”

Section: Research Contributionsmentioning

confidence: 99%

“…Since the granularity of low-level data is too small to contain enough information about surgical technique, we adopt an approach based on k-cos [Hall et al 2008] to aggregate a list of low-level data into a series of surgical strokes and obtain high-level metrics for each stroke, such as stroke distance, speed and force. EPs require nominal data, so we use the entropy-based discretisation method [Fayyad and Irani 1993] to discretise numeric attributes into a number of disjoint intervals.…”

Section: Feedback System Overviewmentioning

confidence: 99%

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Pattern-based real-time feedback for a temporal bone simulator

Zhou

Bailey

Ioannou

et al. 2013

Proceedings of the 19th ACM Symposium on Virtual Reality Software and Technology

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Delivering automated real-time performance feedback in simulated surgical environments is an important and challenging task. We propose a framework based on patterns to evaluate surgical performance and provide feedback during simulated ear (temporal bone) surgery in a 3D virtual environment. Temporal bone surgery is composed of a number of stages with distinct aims and surgical techniques. To provide context-appropriate feedback we must be able to identify each stage, recognise when feedback is to be provided, and determine the nature of that feedback. To achieve these aims, we train pattern-based models using data recorded by a temporal bone simulator. We create one model to predict the current stage of the procedure and separate stage-specific models to provide humanfriendly feedback within each stage. We use 27 temporal bone simulation runs conducted by 7 expert ear surgeons and 6 trainees to train and evaluate our models. The results of our evaluation show that the proposed system identifies the stage of the procedure correctly and provides constructive feedback to assist surgical trainees in improving their technique.

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Towards Haptic Performance Analysis Using K-Metrics

Cited by 10 publications

References 13 publications

Hand Motion Analysis Illustrates Differences When Drilling Cadaveric and Printed Temporal Bone

Hand Motion Analysis Illustrates Differences When Drilling Cadaveric and Printed Temporal Bone

The effect of fidelity: How expert behavior changes in a virtual reality environment

Pattern-based real-time feedback for a temporal bone simulator

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