Ambient Assisted Living (AAL) represents a very relevant area of study since it is of great interest to monitor not only people in need of treatment or rehabilitatn, but also healthy and elderly people. One of the main necessities is to monitor the human gait. However, there are still some open issues to be addressed. Namely, literature shows that there are differences between indoor and outdoor environments. Thus, within the framework of a project to monitor human gait using IMUs (Inertial Measurement Units, MPU-6000 Motion Processing Units), the Enhanced Low Power Real Time (eLPRT) protocol was validated by obtaining the percentage of lost packets (lost protocol messages that contains inertial/magnetic sensors information). Concerning the system, the IMUs were connected to a base station (SmartRF05EB) by means of Radio Frequency (RF). This base station was connected to a personal computer to process the data in real time.In order to validate the communication protocol, the losses of data packets were accounted for during the trials carried out by one subject in three different environments: i) inside a laboratory; ii) in a corridor; and iii) in an outdoor environment. As results, the range of average percentage of loss was 0.52% to 15.21% inside the laboratory; 1.15% to 8.93% in the corridor; and 0.90% to 7.51% in the outdoor environment. The absence of ferromagnetic materials and other wireless communications with the same frequency band are the main reasons why the RF transmission had better results in an outdoor environment.

The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset’s lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future.

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Recently, fall risk assessment has been a main focus in fall-related research. Wearable sensors have been used to increase the objectivity of this assessment, building on the traditional use of oversimplified questionnaires. However, it is necessary to define standard procedures that will us enable to acknowledge the multifactorial causes behind fall events while tackling the heterogeneity of the currently developed systems. Thus, it is necessary to identify the different specifications and demands of each fall risk assessment method. Hence, this manuscript provides a narrative review on the fall risk assessment methods performed in the scientific literature using wearable sensors. For each identified method, a comprehensive analysis has been carried out in order to find trends regarding the most used sensors and its characteristics, activities performed in the experimental protocol, and algorithms used to classify the fall risk. We also verified how studies performed the validation process of the developed fall risk assessment systems. The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous benchmark solution.

A large number of people die around the world in consequence of a fall. The costs related to fatal and nonfatal falls have an enormous impact on society and have been growing over the years. There are several risk factors that increase the probability of falling, such as poor balance and lower extremity weakness. Patients with balance impairment, in order to overcome these problems, use walkers. The aim of this work is to do an analysis of the fall-related strategies already implemented in a smart walker. Therefore, an online search was performed based on the literature through Scopus and Web of Science databases. A study was also conducted on a commercial level on Google, as well as a patent review on Espacenet and United States Trademark Office. It was possible to conclude that exist a concern related to the development of an approach to prevent the fall event. However, the only implemented strategy that was found throughout this research consists in stopping the walker when a near fall is detected.

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Inertial Measurements Unit (IMU) based systems are a purposeful and alternative tool to monitor human gait mainly because they are cheaper, smaller and can be used without space restrictions compared to other gait analysis methods. In the scientific community, there are well-known studies that test the accuracy and efficiency of this method compared to ground truth systems. Gait parameters such as stride length, distance, velocity, cadence, gait phases duration and detection, or joint angles are tested and validated in these studies in order to study and improve this technology. In this article, knee joint angles were calculated from IMUs' data and they were compared with DARwIn OP knee joint angles. IMUs were attached to the left leg of the robot and left knee flexion-extension (F-E) was evaluated. The RMSE values were less than 6 • when DARwIn OP was walking, and less than 5 • when the robot kept the left leg stretched and performed an angle of -30 • .

Fall-related injuries affect a large part of the population and related costs. Thus, there is a concern in studying a fall prevention strategy to minimize the consequences of falls. Walkers are assistive devices used to improve the balance, stability and reduce the load on the lower limb of the user. In this sense, there is a concern to improve the safety in smart walkers and, consequently, to prevent falls in these devices. However, in this field, the only approach is to stop the walker in risk situations. So, the aim of this paper is to define a preliminary strategy to prevent a fall event in the Adaptive System Behaviour Group (ASBGo) Smart Walker. For ASBGo Smart Walker, two modes of security are discussed in this paper. One approach is based on monitoring the center of mass and changing the trajectory when a near fall is detected. The other mechanism consists only in to stop the walker when a dangerous situation is detected. The first or the second mode are activated depending if the user drives the walker with the forearm on forearm support or not.