Design and testing of lightweight inexpensive motion-capture devices with application to clinical gait analysis (original) (raw)
Related papers
Fully portable low-cost motion capture system with real-time feedback for rehabilitation treatment
2019 International Conference on Virtual Rehabilitation (ICVR), 2019
To date, technologically-based rehabilitation methods have been widely used for treating disabilities. The evolution of new technologies utilizing motion capture systems or wearable tracking sensors have further enhanced the standalone self-treatments which benefit both the patients and the physicians. However, current systems have not yet proved to be truly mobile or low-cost, since they do not only need significant processing power to operate or tech-savvy operators, but they also have patients visiting the clinic regularly, more often than expected for a home-based system, in order to receive feedback on their performance. This study presents and proposes a fully portable, low-cost motion capture system that supervises the progress of patients whilst each exercise is being executed; thereby, it provides physicians a more mathematically precise way of evaluating patients’ performance and progress through reports generated by the mobile application. For the purposes of this study, ...
Real-time motion onset recognition for robot-assisted gait rehabilitation
Journal of NeuroEngineering and Rehabilitation
Background Many patients with neurological movement disorders fear to fall while performing postural transitions without assistance, which prevents them from participating in daily life. To overcome this limitation, multi-directional Body Weight Support (BWS) systems have been developed allowing them to perform training in a safe environment. In addition to overground walking, these innovative/novel systems can assist patients to train many more gait-related tasks needed for daily life under very realistic conditions. The necessary assistance during the users’ movements can be provided via task-dependent support designs. One remaining challenge is the manual switching between task-dependent supports. It is error-prone, cumbersome, distracts therapists and patients, and interrupts the training workflow. Hence, we propose a real-time motion onset recognition model that performs automatic support switching between standing-up and sitting-down transitions and other gait-related tasks (8...
Introduction to Low-Cost Motion-Tracking for Virtual Rehabilitation
Biosystems & Biorobotics, 2013
Low-cost motion sensors have seen tremendous increase in popularity in the past few years. Accelerometers, gyroscopes or cameras can be found in most available smart phones and gaming controllers. The Apple Ò iPhone, Nintendo Ò Wii TM and the PlayStatio Ò EyeToy TM are just a few examples where such technology is used to provide a more natural interaction for the user. Depth-sensing cameras by companies such as Microsoft, PrimeSense and Asus can enhance the user experience even further by enabling full-body interaction. This chapter will specifically discuss the use of the Microsoft Ò Kinect TM depth-sensing camera (Kinect) for rehabilitation of patients with motor disabilities. In addition, examples will be provided of how the Kinect can be used with off-the-shelf computer games or utilized in conjunction with modern game development tools such as the game engine Unity. The examples will outline concepts and required resources in order to enable the reader to use low-cost depth-sensing cameras for rehabilitation.
RehabGesture: An Alternative Tool for Measuring Human Movement
Telemedicine and e-Health, 2016
Background: Systems for range of motion (ROM) measurement such as OptoTrak, Motion Capture, Motion Analysis, Vicon, and Visual 3D are so expensive that they become impracticable in public health systems and even in private rehabilitation clinics. Telerehabilitation is a branch within telemedicine intended to offer ways to increase motor and/or cognitive stimuli, aimed at faster and more effective recovery of given disabilities, and to measure kinematic data such as the improvement in ROM. Materials and Methods: In the development of the RehabGesture tool, we used the gesture recognition sensor Kinect Ò (Microsoft, Redmond, WA) and the concepts of Natural User Interface and Open Natural Interaction. Results: RehabGesture can measure and record the ROM during rehabilitation sessions while the user interacts with the virtual reality environment. The software allows the measurement of the ROM (in the coronal plane) from 0°extension to 145°flexion of the elbow joint, as well as from 0°a dduction to 180°abduction of the glenohumeral (shoulder) joint, leaving the standing position. The proposed tool has application in the fields of training and physical evaluation of professional and amateur athletes in clubs and gyms and may have application in rehabilitation and physiotherapy clinics for patients with compromised motor abilities. Conclusions: RehabGesture represents a low-cost solution to measure the movement of the upper limbs, as well as to stimulate the process of teaching and learning in disciplines related to the study of human movement, such as kinesiology.
Toward a low-cost gait analysis system for clinical and free-living assessment
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016
Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to bespoke clinical facilities until recently. The use of inexpensive wearable technologies is an attractive alternative and offers the potential to assess gait in any environment. In this paper we present the development of a low cost analysis gait system built using entirely open source components. The system is used to capture spatio-temporal gait characteristics derived from an existing conceptual model, sensitive to ageing and neurodegenerative pathology (e.g. Parkinson's disease). We demonstrate the system is suitable for use in a clinical unit and will lead to pragmatic use in a free-living (home) environment. The system consists of a wearable (tri-axial accelerometer and gyroscope) with a Raspberry Pi module for data storage and analysis. This forms ongoing work to develop gait as a low cost diagnostic in modern healthcare.
Controlling patient participation during robot-assisted gait training
Journal of NeuroEngineering and Rehabilitation, 2011
Background: The overall goal of this paper was to investigate approaches to controlling active participation in stroke patients during robot-assisted gait therapy. Although active physical participation during gait rehabilitation after stroke was shown to improve therapy outcome, some patients can behave passively during rehabilitation, not maximally benefiting from the gait training. Up to now, there has not been an effective method for forcing patient activity to the desired level that would most benefit stroke patients with a broad variety of cognitive and biomechanical impairments. Methods: Patient activity was quantified in two ways: by heart rate (HR), a physiological parameter that reflected physical effort during body weight supported treadmill training, and by a weighted sum of the interaction torques (WIT) between robot and patient, recorded from hip and knee joints of both legs. We recorded data in three experiments, each with five stroke patients, and controlled HR and WIT to a desired temporal profile. Depending on the patient's cognitive capabilities, two different approaches were taken: either by allowing voluntary patient effort via visual instructions or by forcing the patient to vary physical effort by adapting the treadmill speed. Results: We successfully controlled patient activity quantified by WIT and by HR to a desired level. The setup was thereby individually adaptable to the specific cognitive and biomechanical needs of each patient. Conclusion: Based on the three successful approaches to controlling patient participation, we propose a metric which enables clinicians to select the best strategy for each patient, according to the patient's physical and cognitive capabilities. Our framework will enable therapists to challenge the patient to more activity by automatically controlling the patient effort to a desired level. We expect that the increase in activity will lead to improved rehabilitation outcome.
Spy walker: A convenient way to assess gait in walker assistive devices
2018
This paper presents the Spy Walker, a measurement system intended to characterize gait in walker assistive devices. The proposed system can be easily integrated into any commercial walker without any loss of native functionality. The system makes use of e-textile electrodes to sense the heart rate of the user, load cells to measure the force applied on the walker legs, and an inertial measurement unit to sense motion and orientation. These signals are sampled locally and then transferred over a Bluetooth link to a remote host where they are processed in real time. Data processing includes the detection, classification and characterization of steps. A rich set of parameters is presented for each step, including estimates of unbalance and motor incoordination, travelled distance and azimuth, and lift of the walker frame. This information can be used by a physiotherapist to assess objectively the physical condition of the user, and tune the rehabilitation therapy if needed.
2009
This paper describes a new methodology for gait training and recovery for people in mid-and late-stage dementia. A combination of social and rhythmic auditory cues is used to express the robot's behavior. Specifically, the described research develops and evaluates a method for on-line adaptation aimed at both personalizing the therapy process and maximizing its health-related outcomes. This novel user activity sensing approach provides the rationale for development of socially assistive robotics therapy for monitoring and coaching users toward customized and optimized recovery and care programs.
eFisioTrack: A Telerehabilitation Environment Based on Motion Recognition Using Accelerometry
The Scientific World Journal, 2014
The growing demand for physical rehabilitation processes can result in the rising of costs and waiting lists, becoming a threat to healthcare services’ sustainability. Telerehabilitation solutions can help in this issue by discharging patients from points of care while improving their adherence to treatment. Sensing devices are used to collect data so that the physiotherapists can monitor and evaluate the patients’ activity in the scheduled sessions. This paper presents a software platform that aims to meet the needs of the rehabilitation experts and the patients along a physical rehabilitation plan, allowing its use in outpatient scenarios. It is meant to be low-cost and easy-to-use, improving patients and experts experience. We show the satisfactory results already obtained from its use, in terms of the accuracy evaluating the exercises, and the degree of users’ acceptance. We conclude that this platform is suitable and technically feasible to carry out rehabilitation plans outsid...
Definition of Motion and Biophysical Indicators for Home-Based Rehabilitation through Serious Games
Information
In this paper, we describe Remote Monitoring Validation Engineering System (ReMoVES), a newly-developed platform for motion rehabilitation through serious games and biophysical sensors. The main features of the system are highlighted as follows: motion tracking capabilities through Microsoft Kinect V2 and Leap Motion are disclosed and compared with other solutions; the emotional state of the patient is evaluated with heart rate measurements and electrodermal activity monitored by Microsoft Band 2 during the execution of the functional exercises planned by the therapist. The ReMoVES platform is conceived for home-based rehabilitation after the hospitalisation period, and the system will deploy machine learning techniques to provide an automated evaluation of the patient performance during the training. The algorithms should deliver effective reports to the therapist about the training performance while the patient exercises on their own. The game features that will be described in this manuscript represent the input for the training set, while the feedback provided by the therapist is the output. To face this supervised learning problem, we are describing the most significant features to be used as key indicators of the patient's performance along with the evaluation of their accuracy in discriminating between good or bad patient actions.