Characterisation of rollator use using inertial sensors

The use of walking aids is prevalent among older people and people with mobility impairment. Rollators are designed to support outdoor mobility and require the user to negotiate curbs and slopes in the urban environment. Despite the prevalence of rollators, analysis of their use outside of controlled environments has received relatively little attention. This paper reports on an initial study to characterise rollator movement. An inertial measurement unit (IMU) was used to measure the motion of the rollator and analytical approaches were developed to extract features characterising the rollator movement, properties of the surface, and push events. The analytics were tested in two situations, firstly a healthy participant used a rollator in a laboratory using a motion capture system to obtain ground truth. Secondly the IMU was used to measure the movement of a rollator being used by a user with multiple sclerosis (MS) on a flat surface, cross-slope, up and down slopes, and up and down a step. The results showed that surface inclination and distance travelled measured by the IMU have close approximation to the results from ground truth, therefore demonstrating the potential for IMU-derived metrics to characterise rollator movement and user’s pushing style in the outdoor environment

Validity of the Actigraph GT3X accelerometer in identification of body position and step count in adult hospitalised patients recovering from critical illness

Purpose: Physical recovery from critical illness is complicated by neuromuscular weakness. Evidence suggests mobility commencing within the intensive care unit results in improved function upon discharge. Despite this, persistent inactivity is reported throughout hospital admission. Greater attention should be given to monitoring activity in this setting. Observation and self-report methods may encounter difficulties. Activity monitors (accelerometers) may offer a solution. This PhD thesis aimed to systematically review evidence investigating the validity of accelerometry to quantify purposeful activity within hospitalised adults experiencing acute or critical illness. It also aimed to investigate the validity of the Actigraph GT3X accelerometer in identification of body position (lying, sitting and standing) and step count in patients recovering from critical illness. Methods: A systematic review explored how accelerometer validity had previously been investigated within acute and critically ill hospitalised populations. Another study investigated the feasibility of the GT3X to identify body position and quantify typical activities undertaken by patients’ recovering from critical illness. Thirty healthy participants (mean age 58.8, SD 6.8) simulated this patient group, performing a movement protocol. Twenty ward based patients’ (mean age 62.3, SD 11.5), who had required prolonged ventilation in the ICU (≥ 48 hours) also completed a movement protocol containing typical daily activities. The validity of the GT3X to identify body position and step count was investigated using observation as the criterion measure. Results: A median (interquartile range) of Kappa = 0.94 (0.90, 0.98) for identification of body position was determined interpreting data from two GT3X accelerometers positioned in combination at the ankle and thigh. A mean difference (95% limits of agreement) of -0.84 steps (2.2 to -3.88) compared to observation was found for the ankle placement in step count quantification. Conclusions: The GT3X accelerometer is valid in identification of body position when positioned in combination on the thigh and ankle of the non-dominant leg in patients recovering from critical illness. An ankle placement is valid in quantification of step count

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms

Inertial sensors for smartphones navigation

The advent of smartphones and tablets, means that we can constantly get informa- tion on our current geographical location. These devices include not only GPS/GNSS chipsets but also mass-market inertial platforms that can be used to plan activities, share locations on social networks, and also to perform positioning in indoor and outdoor scenarios. This paper shows the performance of smartphones and their inertial sensors in terms of gaining information about the user’s current geographical loca- tion considering an indoor navigation scenario. Tests were carried out to determine the accuracy and precision obtainable with internal and external sensors. In terms of the attitude and drift estimation with an updating interval equal to 1 s, 2D accuracies of about 15 cm were obtained with the images. Residual benefits were also obtained, however, for large intervals, e.g. 2 and 5 s, where the accuracies decreased to 50 cm and 2.2 m, respectively

Human Gait Analysis in Neurodegenerative Diseases: a Review

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

Mobile Robotics

The book is a collection of ten scholarly articles and reports of experiences and perceptions concerning pedagogical practices with mobile robotics.“This work is funded by CIEd – Research Centre on Education, project UID/CED/01661/2019, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT.

A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%

Hierarchical Reactive Control for Soccer Playing Humanoid Robots

What drives thousands of researchers worldwide to devote their creativity and energy t

Localization and Navigation System for Indoor Mobile Robot

Visually impaired people usually find it hard to travel independently in many public places such as airports and shopping malls due to the problems of obstacle avoidance and guidance to the desired location. Therefore, in the highly dynamic indoor environment, how to improve indoor navigation robot localization and navigation accuracy so that they guide the visually impaired well becomes a problem. One way is to use visual SLAM. However, typical visual SLAM either assumes a static environment, which may lead to less accurate results in dynamic environments or assumes that the targets are all dynamic and removes all the feature points above, sacrificing computational speed to a large extent with the available computational power. This paper seeks to explore marginal localization and navigation systems for indoor navigation robotics. The proposed system is designed to improve localization and navigation accuracy in highly dynamic environments by identifying and tracking potentially moving objects and using vector field histograms for local path planning and obstacle avoidance. The system has been tested on a public indoor RGB-D dataset, and the results show that the new system improves accuracy and robustness while reducing computation time in highly dynamic indoor scenes.Comment: Accepted by the 2023 5th International Conference on Materials Science, Machine and Energy Engineerin