## 1 Introduction  
Gestures are composed of multiple body-part motions and can form activities [1]. Hence, gesture recognition offers a wide range of applications, including inter alia, fitness training, human robot and computer interaction, security, and sign language recognition. Likewise, gesture recognition is employed in ambient assisted living systems for tackling burgeoning and worrying public healthcare problems, such as autonomous living for people with dementia and Parkinson's disease. Although a large amount of work has been conducted on image-based sensing technology, camera and depth sensors are limited to the environment in which they are installed. Moreover, they are sensitive to obstructions in the field of vision, variation in luminous intensity, reflection, etc. In contrast, wearable sensors and mobile devices are more suitable for monitoring ambulatory activities and physiological signals. In a supervised context, a wide range of action or gesture recognition techniques has been explored using wearable sensors. k-Nearest Neighbor (k-NN) might be the most straightforward classifier to utilize since it does not learn but searches the closest data in the training data using a given distance function. Even though conventional k-NN achieves good performance, it suffers from lack of ability to deal with these problems: low attribute and sample noise tolerance, high-dimensional spaces, large training dataset requirements, and imbalances in the data. [PERSON] et al. [2] recently proposed a random subspace ensemble framework based on hybrid k-NN to tackle these problems, but the classifier has not yet been applied to a gesture recognition task. Hidden Markov Model (HMM) is the most traditional probabilistic method used in the literature [3; 4]. However, computing transition probabilities necessary for learning model parameters requires a large amount of training data. HMM-based techniques may also not be suitable for hard real-time (synchronized clock-based) systems due to its latency [5]. Since data sets are not necessarily large enough for training, Support Vector Machine (SVM) is a classical alternative method [6; 7; 8]. SVM is, nevertheless, very sensitive to the selection of its kernel type and parameters related to the latter. There are novel dynamic Bayesian networks often used to deal with sequence analysis, such as recurrent neural networks (e.g., LSTMs) [9] and deep learning approach [10], which should become more popular in the next years. Dynamic Time Warping (DTW) is one of the most utilized similarity measures for matching two time-series sequences [11; 12]. Often reproached for being slow, [PERSON] et al. [13] demonstrated that DTW is quicker than Euclidean distance search algorithms and even suggests that the method can spot gestures in real time. However, the recognition performance of DTW is affected by the strong presence of noise, caused by either segmentation of gestures during the training phase or gesture execution variability. The longest common subsequence (LCSS) method is a precursor to DTW. It measures the closeness of two sequences of symbols corresponding to the length of the longest subsequence common to these two sequences. One of the abilities of DTW is to deal with sequences of different lengths, and this is the reason why it is often used as an alignment method. In [14], LCSS was found to be more robust in noisy conditions than DTW. Indeed, since all elements are paired in DTW, noisy elements (i.e., unwanted variation and outliers) are also included, while they are simply ignored in the LCSS. Although some image-based gesture recognition applications can be found in [15; 16; 17], not much work has been conducted using non-image data. In the context of crowd-sourced annotations, [PERSON] et al. [18] proposed two methods, entitled SegmentedLCSS and WarpingLCSS.