Purpose: Basic surgical skills of suturing and knot tying are an essential
part of medical training. Having an automated system for surgical skills
assessment could help save experts time and improve training efficiency. There
have been some recent attempts at automated surgical skills assessment using
either video analysis or acceleration data. In this paper, we present a novel
approach for automated assessment of OSATS based surgical skills and provide an
analysis of different features on multi-modal data (video and accelerometer
data). Methods: We conduct the largest study, to the best of our knowledge, for
basic surgical skills assessment on a dataset that contained video and
accelerometer data for suturing and knot-tying tasks. We introduce "entropy
based" features - Approximate Entropy (ApEn) and Cross-Approximate Entropy
(XApEn), which quantify the amount of predictability and regularity of
fluctuations in time-series data. The proposed features are compared to
existing methods of Sequential Motion Texture (SMT), Discrete Cosine Transform
(DCT) and Discrete Fourier Transform (DFT), for surgical skills assessment.
Results: We report average performance of different features across all
applicable OSATS criteria for suturing and knot tying tasks. Our analysis shows
that the proposed entropy based features out-perform previous state-of-the-art
methods using video data. For accelerometer data, our method performs better
for suturing only. We also show that fusion of video and acceleration features
can improve overall performance with the proposed entropy features achieving
highest accuracy. Conclusions: Automated surgical skills assessment can be
achieved with high accuracy using the proposed entropy features. Such a system
can significantly improve the efficiency of surgical training in medical
schools and teaching hospitals.

本文介绍了一种基于熵的特征来自动评估 OSATS 基本手术技能的方法，并分析了多模数据（视频和加速度计数据）上不同的特征。结果显示，所提出的熵特征优于现有的视频数据分析的方法，而加速度计数据则仅在缝合中的恢复表现优异，融合视频和加速度数据可以提高总体性能。

基于视频和加速度计的自动手术技能评估运动分析

Video and Accelerometer-Based Motion Analysis for Automated Surgical  Skills Assessment

The problem of human activity recognition is central for understanding and
predicting the human behavior, in particular in a prospective of assistive
services to humans, such as health monitoring, well being, security, etc. There
is therefore a growing need to build accurate models which can take into
account the variability of the human activities over time (dynamic models)
rather than static ones which can have some limitations in such a dynamic
context. In this paper, the problem of activity recognition is analyzed through
the segmentation of the multidimensional time series of the acceleration data
measured in the 3-d space using body-worn accelerometers. The proposed model
for automatic temporal segmentation is a specific statistical latent process
model which assumes that the observed acceleration sequence is governed by
sequence of hidden (unobserved) activities. More specifically, the proposed
approach is based on a specific multiple regression model incorporating a
hidden discrete logistic process which governs the switching from one activity
to another over time. The model is learned in an unsupervised context by
maximizing the observed-data log-likelihood via a dedicated
expectation-maximization (EM) algorithm. We applied it on a real-world
automatic human activity recognition problem and its performance was assessed
by performing comparisons with alternative approaches, including well-known
supervised static classifiers and the standard hidden Markov model (HMM). The
obtained results are very encouraging and show that the proposed approach is
quite competitive even it works in an entirely unsupervised way and does not
requires a feature extraction preprocessing step.

通过对三维空间内由佩戴式加速度计测量的加速度数据的多维时间序列进行分割，本文分析了活动识别的问题，并提出了一种特定的统计潜在过程模型进行自动时段分割，该模型通过隐藏的离散逻辑过程来控制随时间而变换的多个活动。最终结果表明，本文提出的方法在完全无监督的情况下运作，并且不需要特征提取预处理步骤，其性能非常有竞争力。