This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

This article presents a machine learning methodology for diagnosing Parkinson’s disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model (GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collected within three different studies is used. This data set includes vGRF measurements collected from eight force sensors placed under each foot of the subjects. Ninety-three patients suffering from Parkinson’s disease and 72 healthy subjects participated in the experiments. The obtained performances are compared with respect to various metrics including accuracy, precision, recall and F-measure. The classification performance evaluation is performed using the leave-one-out cross validation. The results demonstrate the ability of the proposed methodology to accurately differentiate between PD subjects and healthy subjects. For the purpose of validation, the proposed methodology is also evaluated with an additional dataset including subjects with neurodegenerative diseases (Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD)). The obtained results show the effectiveness of the proposed methodology to discriminate PD subjects from subjects with other neurodegenerative diseases with a relatively high accuracy.

This paper presents a new approach for automatic recognition of gait phases based on the use of an in-shoe pressure measurement system and a Multiple Regression Hidden Markov Model (MRHMM) that takes into account the sequential completion of the gait phases. Recognition of gait phases is formulated as a multiple polynomial regression problem in which each phase, called a segment, is modelled using an appropriate polynomial function. The MRHMM is learned in an unsupervised manner to avoid manual data labelling, which is a laborious, time-consuming task that is subject to potential errors, particularly for large amounts of data. To evaluate the efficiency of the proposed approach, several performance metrics for classification are used: accuracy, F-measure, recall and precision. Experiments conducted with 5 subjects during walking show the potential of the proposed method to recognize gait phases with relatively high accuracy. The proposed approach outperforms standard unsupervised classification methods (GMM, k-Means and HMM) while remaining competitive with respect to standard supervised classification methods (SVM, RF and k-NN).

One of the main objectives of Ambient Assisted Living (AAL) systems is to proactively provide intelligent services to improve the quality of people's lives in terms of autonomy, safety, and well-being. Designing AAL systems that can autonomously monitor human's activities and provide assistance services poses several challenges of which Human Activity Recognition (HAR) which is critically important to adapt the assistance services to the user. In this paper, a robust multi-label HAR framework is proposed. The proposed framework is composed of two main modules: (i) activity classification module and (ii) classification error detection and correction module. In the first module, machine-learning models are used to predict human activities. Since these models may produce predictions with errors, there is a requirement to detect and correct these errors. The classification error detection and correction module is based on two acyclic directed graphical models and operates in two phases: (i) classification error detection and (ii) classification error correction. The proposed framework is evaluated on the Opportunity dataset, a benchmark and unique dataset for multi-label human daily living activity recognition. The obtained results demonstrate the ability of the proposed framework to improve the performances of HAR.

Driving errors are considered to be the greatest contributory cause in all road accidents and an important contributory cause of most fatal accidents. This is particularly the case for the users of powered two-wheeled vehicles (PTWs), perhaps because PTW riders play a greater role in the control of their vehicles' stability than four-wheeled vehicle drivers. Thus, observing and analyzing the evolution of riders' behavior in a real-life context is an important step in the identification of the road environment characteristics that constitute a risk factor for PTW riders. A relevant research issue in naturalistic studies is related to the detection and identification of critical riding events from among the vast amount of data recorded during the experiment. In this paper, two approaches were used to automatically detect such critical riding events. First, we formalized this problem in terms of detecting changes in the mean and variance of the signals generated by the acceleration and angular velocity sensors. For this purpose, two steps were performed: (1) a data segmentation and feature extraction step in which the multidimensional time series of accelerometer and angular velocity data were segmented and modeled using a Gaussian mixture model with quadratic logistic proportions; and (2) a classification step in which each detected segment was assigned to the appropriate riding sequence, whether “naturalistic” or “critical” (i.e., fall or near fall), using the k-nearest neighbor algorithm. The second approach was based on online fall detection. This methodology used control charts (a multivariate cumulative sum), an approach that has been traditionally employed for sequential detection. These two algorithms were applied to a database composed of data from a real-life driving experiment. The obtained results show the effectiveness of both methodologies.