*1.2. Literature Review and Proposed Work*

Traditionally, studies of XC-skiing techniques have been limited to the kinematical and biomechanical analysis of various techniques [12,13]. These studies aim to determine numerous hard rules, like cycle time, poling/pushing time, recovery time [5,14,15], number of recovery motions, the sign of forearm angular velocity [16,17], correlation of the angular velocity of arms and legs [18], and figures showing identifiable cyclic patterns in the gyroscope and accelerometer data for the classification of techniques. These approaches, however, are extremely time-consuming as the derivation of the classification rules requires manual analysis of the gyroscope and accelerometer data from multiple sensors. Recently, many researchers have analyzed and classified techniques of XC-skiing using algorithms, like markov chains of multivariate distributions, and more advanced machine learning techniques. Stoggl et al. [19] utilized an accelerometer attached to the chest of professional skiing skiers to classify skating techniques. They collected data of 11 skiing skiers on a treadmill and developed a classification model based on the markov chains of multivariate distributions. Their model achieved an accuracy of 86% ± 8.9% on the test set when the training data included data from all the skiers, which rose to 90.3% ± 4.1% when separate classification models were developed for each skier. Rindal et al. [1] utilized a neural network for the classification of skating techniques by utilizing two sensors-a gyroscope on the arm for cycle identification and an accelerometer on the chest for technique classification. They achieved an accuracy of 93.9% on the test set. In both studies, the raw data was passed through a gaussian filter for the removal of ringing effects and undesirable time shifts at different frequencies. Ristner [20] implemented a Markov model and a k-nearest neighbors (KNN) algorithm for classifying XC skiing techniques using a 3D accelerometer attached on the chest of the skiers. The comparison showed that the KNN algorithm showed much lower error rates (0.19%) than the Markov model (7.22%). All these studies are impressive and show promise for the automatic and reliable classification of XC-skiing techniques using inertial sensors. However, these studies suffer from many limitations. In the study performed by [19], the data is collected in the controlled environment of the laboratory, which will be different from the actual on-field data. The neural network model used by [1] takes in data of each cycle after flattening it into a single vector. This leads to information loss as the spatial and temporal patterns in the data are lost. Both studies develop models that classify either only the classical or skating techniques and do not obtain a single model, which could be employed for both the styles. Table 1 summarizes the relevant details of the aforementioned studies.

In this paper, we propose a unified convolutional neural network (CNN) and long-short term memory (LSTM) based deep learning classification model, which can be used to classify both the classical and skating style techniques of the skiers simultaneously. The first novelty of our approach lies in using convolutional layers for merging the local interactions among the time-series data obtained from each sensor and recurrent layers (long-short term memory layers) for extracting the temporal patterns. In this way, the model is able to extract important features for the classification of various techniques automatically from the raw data, thus eliminating the need for manually designed features required by machine learning algorithms. To prove this point, we present a comparison of the results obtained from our model with a KNN model developed by manually extracting features on the same training, validation, and test datasets. We collected the flat and natural course data of four professional skiers in total and pose a working hypothesis that the generalization accuracy of the proposed deep learning model increases as the amount of training data is increased. To prove this point, the model is first trained on the flat course data of two professional skiers and tested on the flat and natural course data of a third skier in a leave-one-out fashion. Secondly, the model is trained over the flat course data of three skiers and tested on the flat and natural course data of the fourth skier. An increase in the accuracy of classification when the size of the training data is increased confirms this hypothesis.

The second novelty of our approach lies in developing a unified model, which can be used for the classification of both classical and skating techniques simultaneously. We present strong evidence in favor of using only the flat course data for training the model and using it to classify XC-skiing techniques both on flat and natural courses, thus eliminating the need for collecting natural course data for training, which is extremely difficult to procure. Finally, the comparison of accuracies among five different combinations of sensors, which establishes the sports biomechanics configuration (both hands, both feet, and the pelvis sensors) as the optimal set of sensors, provides empirical evidence to researchers to base their future studies on this optimal configuration.


