Posts Tagged IMU

[Conference Paper] Modelling of a wearable jacket with sensors and actuators for upper limb rehabilitation


Introduction Spinal Cord Injury (SCI) affects a large number of young people and, if left  untreated, can deal irreversible damage to the human body. Several studies have demonstrated the positive impact of physical therapy to the rehabilitation process, promoting neuro-plasticity and thus at least partial restoration of functionality of the body and gait. These studies focus on the implementation of engineered solutions, such as robotic exoskeletons and virtual reality training regimens. The common denominator in most of them is the implementation of some form of Human-Machine Interface (HMI), for the control of these modalities by direct user feedback. These HMIs are based on a plethora of sensor arrays, ranging from direct motion-specific body data, such as Electroencephalography (EEG) and Electromyography (EMG) to more common sensor devices, such as accelerometers and gyroscopes. These sensors can provide direct measurements, tailored to the application at hand and provide the necessary data for the desired functionality. Materials and Methods The proposed device will function as a sensor array for the upper-body, providing live data for muscle activity, through the use of Electromyography (EMG) electrodes, as well as relative joint positioning and rotation, utilizing Inertial Measurement Units (IMUs), for the purpose of monitoring and Augmented Reality (AR) integration. Said motion data will be then used to enhance the users desired movement, through the use of Functional Electronic Stimulation (FES), by providing the necessary impulse to each muscle group, from the measured feedback. The relationship between sensor input and stimulation will allow for reinforcement of the users’ movements, promoting neuroplasticity and ease of movement in the process of neuro-rehabilitation. Furthermore, this modality will act as a platform for several other physiological measurements, such as heart rate and perspiration, essentially creating a functional Body-Area Network (BAN) of sensors. Integration with external motion actuators will be investigated, as a means to provide upper-body support, providing the necessary strength, as a means of easing the rehabilitation process and removing unnecessary stress from the user. Finally, interactions with implanted medical devices will be explored. Such devices could provide telemetry data from inside the body, to be used as a form of direct feedback for the designed Body Area Network (BAN), and the aforementioned stimulation and actuation.

via Modelling of a wearable jacket with sensors and actuators for upper limb rehabilitation

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[ARTICLE] Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations – Full Text



Model based analysis of human upper limb movements has key importance in understanding the motor control processes of our nervous system. Various simulation software packages have been developed over the years to perform model based analysis. These packages provide computationally intensive—and therefore off-line—solutions to calculate the anatomical joint angles from motion captured raw measurement data (also referred as inverse kinematics). In addition, recent developments in inertial motion sensing technology show that it may replace large, immobile and expensive optical systems with small, mobile and cheaper solutions in cases when a laboratory-free measurement setup is needed. The objective of the presented work is to extend the workflow of measurement and analysis of human arm movements with an algorithm that allows accurate and real-time estimation of anatomical joint angles for a widely used OpenSim upper limb kinematic model when inertial sensors are used for movement recording.


The internal structure of the selected upper limb model is analyzed and used as the underlying platform for the development of the proposed algorithm. Based on this structure, a prototype marker set is constructed that facilitates the reconstruction of model-based joint angles using orientation data directly available from inertial measurement systems. The mathematical formulation of the reconstruction algorithm is presented along with the validation of the algorithm on various platforms, including embedded environments.


Execution performance tables of the proposed algorithm show significant improvement on all tested platforms. Compared to OpenSim’s Inverse Kinematics tool 50–15,000x speedup is achieved while maintaining numerical accuracy.


The proposed algorithm is capable of real-time reconstruction of standardized anatomical joint angles even in embedded environments, establishing a new way for complex applications to take advantage of accurate and fast model-based inverse kinematics calculations.

Continue —> Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations | BioMedical Engineering OnLine | Full Text

Fig. 1 Representations of the used upper limb model with reference poses and markers. a Screenshot taken from OpenSim while displaying the used full arm model. The reference configuration is shown as a shaded overlay on an actual example configuration determined by the joint angle vector [θelvθelv = 0∘0∘, θsh_elvθsh_elv = 63∘63∘, θsh_rotθsh_rot = 15∘15∘, θel_flexθel_flex = 95∘95∘, θpro_supθpro_sup = −60∘−60∘, θdev_cθdev_c = 0∘0∘, θflex_cθflex_c = 20∘20∘]. b Representation of the model’s exported structure in MATLAB producing the same actual configuration as in sub-figure (a) using the developed forward kinematics function (functionally equivalent to OpenSim’s version). c Locations of prototype markers that are solely used to the reconstruction of model-defined anatomical joint angles with the proposed algorithm. d Locations of virtual markers that are used for the algorithm validation process by serving as inputs to OpenSim’s inverse kinematics tool directly

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[ARTICLE] Assessment-driven arm therapy at home using an IMU-based virtual reality system


Virtual reality therapy systems have the potential to increase the intensity and frequency of physical activity of stroke patients at home. This might help to increase the dose of rehabilitation, without the costs associated with clinic visits and therapist supervision.

We present a therapy game that continuously estimates the patient’s arm reachable three-dimensional (3D) workspace with a voxel-based model and selects targets to be reached accordingly, in order to increase challenge without causing frustration. This exercise is implemented on a novel, inertial measurement unit (IMU) based virtual reality system for the training of upper limb function. We present data from a pilot trial with 5 chronic stroke patients who trained for 6 weeks at home and without therapist supervision.

On average, the patients’ in-game assessed 3D workspace grew by 10.7% in volume and their score on the Fugl-Meyer Upper Extremity score improved by 5 points. The average self-selected therapy time, over the course of the therapy, was 16.8 h. These results suggest that the proposed assessment-driven target selection is viable for unsupervised home therapy and could form the basis for additional therapy games in the future.

Source: IEEE Xplore Abstract – Assessment-driven arm therapy at home using an IMU-based virtual reality system

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[WEB SITE] Researchers develop low-cost stroke rehabilitation glove

When the use of a hand is lost due to a stroke, it’s important to get that paralyzed hand moving again – this allows the brain and the body to “relearn” how to use it. A new approach to this problem has emerged in recent years with the development of powered devices like the Amadeo or the Rehabilitation Glove that enable patients to exercise passively until they recover sufficiently to start moving on their own…

via Researchers develop low-cost stroke rehabilitation glove.

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