This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined.

Over the last decade, there has been considerable and increasing interest in the development of Active and Assisted Living (AAL) systems to support independent living. The demographic change towards an aging population has introduced new challenges to today’s society from both an economic and societal standpoint. AAL can provide an arrary of solutions for improving the quality of life of individuals, for allowing people to live healthier and independently for longer, for helping people with disabilities, and for supporting caregivers and medical staff. A vast amount of literature exists on this topic, so this paper aims to provide a survey of the research and skills related to AAL systems. A comprehensive analysis is presented that addresses the main trends towards the development of AAL systems both from technological and methodological points of view and highlights the main issues that are worthy of further investigation.

In recent years, the number of older people living alone has increased rapidly. Innovative vision systems to remotely assess people’s mobility can help healthy, active, and happy aging. In the related literature, the mobility assessment of older people is not yet widespread in clinical practice. In addition, the poor availability of data typically forces the analyses to binary classification, e.g. normal/anomalous behavior, instead of processing exhaustive medical protocols. In this paper, real videos of elderly people performing three mobility tests of a clinical protocol are automatically categorized, emulating the complex evaluation process of expert physiotherapists. Videos acquired using low-cost cameras are initially processed to obtain skeletal information. A proper data augmentation technique is then used to enlarge the dataset variability. Thus, significant features are extracted to generate a set of inputs in the form of time series. Four deep neural network architectures with feedback connections, even aided by a preliminary convolutional layer, are proposed to label the input features in discrete classes or to estimate a continuous mobility score as the result of a regression task. The best results are achieved by the proposed Conv-BiLSTM classifier, which achieves the best accuracy, ranging between 88.12% and 90%. Further comparisons with shallow learning classifiers still prove the superiority of the deep Conv-BiLSTM classifier in assessing people’s mobility, since deep networks can evaluate the quality of test executions.

This article proposes an automatic approach for segmenting inclusion defects in composite materials inspected by lock-in thermography (LT) in a heat source invariant way. In the proposed pipeline, temperature maps are preliminarily processed to enhance the contrast between sound and defective areas, reduce the acquisition noise, and mitigate the effect of nonuniform heat excitation. Then, the enhanced sequences are grouped in highly local sets, the input of a convolution residual neural network, which segments the defects focusing on the temporal evolution of the temperature. Experiments have been run on a dataset acquired from a CFRP specimen tested under five different heat source configurations. Here, a brief study of signal variations associated with the heat source configuration is presented. Then, an ablation study has been presented to compare different architectures of increasing complexity. The best results are achieved by using the proposed preliminary processing, the residual layers, and an input set of temperature signals of size $3 \times 3$ . This network can reach a global intersection over union (IOU) of 0.78, while defects are segmented with an IOU value of 0.58. In addition, the balanced segmentation accuracy of the test reaches 94.78%, with a maximum recall of the defective class of 91.41%. Further cross validation changing the source configurations of the training and test sets produces comparable results of IOU and balanced accuracy, which are on average equal to 0.71 and 84.02%, respectively. A final evaluation of the trained network is performed on a new dataset augmented through a vertical stretch. Results are still comparable with an average IOU and balanced accuracy of 0.74 and 87.15%, respectively. These results prove the capability of the method to segment inclusion defects of any shape without being affected by the configuration of the heating sources used in the LT inspections.

In this paper, a new method for a robust and efficient analysis of video sequences is presented; it allows the extraction of foreground objects and the classification of static foreground regions as abandoned or removed objects (ghosts). As a first step, the moving regions in the scene are detected by subtracting to the current frame a background model continuously adapted. Then, a shadow removing algorithm is used to extract the real shape of detected objects. Finally, moving objects are classified as abandoned or removed by matching the boundaries of static foreground regions. The method was successfully tested on both real image sequences acquired in our laboratory and some sequences from the PETS 2006 Datasets.