Development and preliminary evaluation of a novel low cost VR-based upper limb stroke rehabilitation platform using Wii technology.

Abstract Purpose: This paper proposes a novel system (using the Nintendo Wii remote) that offers customised, non-immersive, virtual reality-based, upper-limb stroke rehabilitation and reports on promising preliminary findings with stroke survivors. Method: The system novelty lies in the high accuracy of the full kinematic tracking of the upper limb movement in real-time, offering strong personal connection between the stroke survivor and a virtual character when executing therapist prescribed adjustable exercises/games. It allows the therapist to monitor patient performance and to individually calibrate the system in terms of range of movement, speed and duration. Results: The system was tested for acceptability with three stroke survivors with differing levels of disability. Participants reported an overwhelming connection with the system and avatar. A two-week, single case study with a long-term stroke survivor showed positive changes in all four outcome measures employed, with the participant reporting better wrist control and greater functional use. Activities, which were deemed too challenging or too easy were associated with lower scores of enjoyment/motivation, highlighting the need for activities to be individually calibrated. Conclusions: Given the preliminary findings, it would be beneficial to extend the case study in terms of duration and participants and to conduct an acceptability and feasibility study with community dwelling survivors. Implications for Rehabilitation Low-cost, off-the-shelf game sensors, such as the Nintendo Wii remote, are acceptable by stroke survivors as an add-on to upper limb stroke rehabilitation but have to be bespoked to provide high-fidelity and real-time kinematic tracking of the arm movement. Providing therapists with real-time and remote monitoring of the quality of the movement and not just the amount of practice, is imperative and most critical for getting a better understanding of each patient and administering the right amount and type of exercise. The ability to translate therapeutic arm movement into individually calibrated exercises and games, allows accommodation of the wide range of movement difficulties seen after stroke and the ability to adjust these activities (in terms of speed, range of movement and duration) will aid motivation and adherence - key issues in rehabilitation. With increasing pressures on resources and the move to more community-based rehabilitation, the proposed system has the potential for promoting the intensity of practice necessary for recovery in both community and acute settings.The National Health Service (NHS) London Regional Innovation Fund

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error)

Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers

In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the humanarm (extension, flexion and rotation of the forearm) involved in ‘making-a-cup-of-tea’, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier

Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn)

This paper presents the application of neural networks for analysing voltage stability of load buses in electric power system. Voltage stability margin (VSM) and load power margin (LPM) are used as the indicators for analysing voltage stability. The neural networks used in this research are divided into two types. The first type is using the neural network to predict the values of VSM and LPM. Multilayer perceptron back propagation (MLPBP) neural network and adaptive neuro-fuzzy inference system (ANFIS) will be used. The second type is to classify the values of VSM and LPM using the probabilistic neural network (PNN). The IEEE 30-bus system has been chosen as the reference electrical power system. All of the neural network-based models used in this research is developed using MATLAB

A Narrative Review on Wearable Inertial Sensors for Human Motion Tracking in Industrial Scenarios

Industry 4.0 has promoted the concept of automation, supporting workers with robots while maintaining their central role in the factory. To guarantee the safety of operators and improve the effectiveness of the human-robot interaction, it is important to detect the movements of the workers. Wearable inertial sensors represent a suitable technology to pursue this goal because of their portability, low cost, and minimal invasiveness. The aim of this narrative review was to analyze the state-of-the-art literature exploiting inertial sensors to track the human motion in different industrial scenarios. The Scopus database was queried, and 54 articles were selected. Some important aspects were identified: (i) number of publications per year; (ii) aim of the studies; (iii) body district involved in the motion tracking; (iv) number of adopted inertial sensors; (v) presence/absence of a technology combined to the inertial sensors; (vi) a real-time analysis; (vii) the inclusion/exclusion of the magnetometer in the sensor fusion process. Moreover, an analysis and a discussion of these aspects was also developed

Low Cost Inertial Sensors for the Motion Track-ing and Orientation Estimation of Human Upper Limbs in Neurological Rehabilitation

This paper presents the feasibility of utilizing low cost inertial sensors such as those found in Sony Move, Nintendo Wii (Wii Remote with Wii MotionPlus) and smartphones for upper limb motion mon-itoring in neurorehabilitation. Kalman and complementary filters based on data fusion are used to estimate sensor 3D orientation. Furthermore, a two-segment kinematic model was developed to estimate limb segment position tracking. Performance has been compared with a high-accuracy measurement system using the Xsens MTx. The experimental results show that Sony Move, Wii and smartphones can be used for measuring upper limb orientation, while Sony Move and smartphones can also be used for specific applications of upper limb segment joint orientation and position tracking during neurorehabilitation. Sony Move’s accuracy is within 1.5° for Roll and Pitch and 2.5° for Yaw and position tracking to within 0.5 cm over a 10 cm movement. This accuracy in measurement is thought to be adequate for upper limb orientation and position tracking. Low cost inertial sensors can be used for the accurate assessment/measurement of upper limb movement of patients with neurological disorders and also makes it a low cost replacement for upper limb motion measurements. The low cost inertial sensing systems were shown to be able to accurately measure upper limb joint orienta-tion and position during neurorehabilitation

Measurement of Upper Limb Range of Motion Using Wearable Sensors: A Systematic Review.

Background: Wearable sensors are portable measurement tools that are becoming increasingly popular for the measurement of joint angle in the upper limb. With many brands emerging on the market, each with variations in hardware and protocols, evidence to inform selection and application is needed. Therefore, the objectives of this review were related to the use of wearable sensors to calculate upper limb joint angle. We aimed to describe (i) the characteristics of commercial and custom wearable sensors, (ii) the populations for whom researchers have adopted wearable sensors, and (iii) their established psychometric properties. Methods: A systematic review of literature was undertaken using the following data bases: MEDLINE, EMBASE, CINAHL, Web of Science, SPORTDiscus, IEEE, and Scopus. Studies were eligible if they met the following criteria: (i) involved humans and/or robotic devices, (ii) involved the application or simulation of wearable sensors on the upper limb, and (iii) calculated a joint angle. Results: Of 2191 records identified, 66 met the inclusion criteria. Eight studies compared wearable sensors to a robotic device and 22 studies compared to a motion analysis system. Commercial (n = 13) and custom (n = 7) wearable sensors were identified, each with variations in placement, calibration methods, and fusion algorithms, which were demonstrated to influence accuracy. Conclusion: Wearable sensors have potential as viable instruments for measurement of joint angle in the upper limb during active movement. Currently, customised application (i.e. calibration and angle calculation methods) is required to achieve sufficient accuracy (error < 5°). Additional research and standardisation is required to guide clinical application

Validation of a Biomechanical Injury and Disease Assessment Platform Applying an Inertial-Based Biosensor and Axis Vector Computation

Inertial kinetics and kinematics have substantial influences on human biomechanical function. A new algorithm for Inertial Measurement Unit (IMU)-based motion tracking is presented in this work. The primary aims of this paper are to combine recent developments in improved biosensor technology with mainstream motion-tracking hardware to measure the overall performance of human movement based on joint axis-angle representations of limb rotation. This work describes an alternative approach to representing three-dimensional rotations using a normalized vector around which an identified joint angle defines the overall rotation, rather than a traditional Euler angle approach. Furthermore, IMUs allow for the direct measurement of joint angular velocities, offering the opportunity to increase the accuracy of instantaneous axis of rotation estimations. Although the axis-angle representation requires vector quotient algebra (quaternions) to define rotation, this approach may be preferred for many graphics, vision, and virtual reality software applications. The analytical method was validated with laboratory data gathered from an infant dummy leg’s flexion and extension knee movements and applied to a living subject’s upper limb movement. The results showed that the novel approach could reasonably handle a simple case and provide a detailed analysis of axis-angle migration. The described algorithm could play a notable role in the biomechanical analysis of human joints and offers a harbinger of IMU-based biosensors that may detect pathological patterns of joint disease and injury

Hybrid Position and Orientation Tracking for a Passive Rehabilitation Table-Top Robot

This paper presents a real time hybrid 2D position and orientation tracking system developed for an upper limb rehabilitation robot. Designed to work on a table-top, the robot is to enable home-based upper-limb rehabilitative exercise for stroke patients. Estimates of the robot's position are computed by fusing data from two tracking systems, each utilizing a different sensor type: laser optical sensors and a webcam. Two laser optical sensors are mounted on the underside of the robot and track the relative motion of the robot with respect to the surface on which it is placed. The webcam is positioned directly above the workspace, mounted on a fixed stand, and tracks the robot's position with respect to a fixed coordinate system. The optical sensors sample the position data at a higher frequency than the webcam, and a position and orientation fusion scheme is proposed to fuse the data from the two tracking systems. The proposed fusion scheme is validated through an experimental set-up whereby the rehabilitation robot is moved by a humanoid robotic arm replicating previously recorded movements of a stroke patient. The results prove that the presented hybrid position tracking system can track the position and orientation with greater accuracy than the webcam or optical sensors alone. The results also confirm that the developed system is capable of tracking recovery trends during rehabilitation therapy