Felipe Orihuela-Espina

New developments in the Gesture Therapy platform: Past, present and future of our research

Gesture Therapy (GT) is a virtual rehabilitation tool for the upper arm that has been in the making since 2008, and by now has successfully demonstrated therapeutic validity in two small clinical trials for stroke survivors. During this time, our group has published a number of contributions regarding different aspects of this platform ranging from hardware controllers to artificial intelligence algorithms guiding different aspects of the serious games behaviour, and clinical trial data from observable improvements in dexterity to changes in functional neuroreorganization. As we continue our research efforts in virtual rehabilitation and realising this knowledge in the GT platform, this paper presents an overview of the latest developments as well as a roadmap for future research.

Developing a device for monitoring O2 saturation in blood

We present a single-channel device for monitoring oxygen saturation in blood using near infrared light (NIR). This device measures the differential absorption of both oxi-hemoglobin (HbO2) and deoxi-hemoglobin (HHb) concentrations using wavelengths within the biophotonic window (700 nm – 1100 nm). Our device works with two wavelengths: the first source (λ1 = 632 nm) operates in continuous wave (CW) and the second (λ2 = 940 nm) operates in pulsed mode. The pulsed signal at λ2 operates in a 20% fill factor. The CW signal provides information related to the arterial pulse, whereas both CW and pulsed signal provide information about blood oxygenation. Light emitted from the sources travels through the tissue and are collected on the detector by transmission. Measurements of blood oxygenation measured in transmission on fingers tips are presented which are comparable to commercial devices. The values obtained are above 90.0% blood oxygenation in healthy tests subjects with 0.1% accuracy in the measurements.

Modelling and validation of diffuse reflectance of the adult human head for fNIRS: scalp sub-layers definition.

Accurate estimation of brain haemodynamics parameters such as cerebral blood flow and volume as well as oxygen consumption i.e. metabolic rate of oxygen, with funcional near infrared spectroscopy (fNIRS) requires precise characterization of light propagation through head tissues. An anatomically realistic forward model of the human adult head with unprecedented detailed specification of the 5 scalp sublayers to account for blood irrigation in the connective tissue layer is introduced. The full model consists of 9 layers, accounts for optical properties ranging from 750nm to 950nm and has a voxel size of 0.5mm. The whole model is validated comparing the predicted remitted spectra, using Monte Carlo simulations of radiation propagation with 108 photons, against continuous wave (CW) broadband fNIRS experimental data. As the true oxy- and deoxy-hemoglobin concentrations during acquisition are unknown, a genetic algorithm searched for the vector of parameters that generates a modelled spectrum that optimally fits the experimental spectrum. Differences between experimental and model predicted spectra was quantified using the Root mean square error (RMSE). RMSE was 0.071 ± 0.004, 0.108 ± 0.018 and 0.235±0.015 at 1, 2 and 3cm interoptode distance respectively. The parameter vector of absolute concentrations of haemoglobin species in scalp and cortex retrieved with the genetic algorithm was within histologically plausible ranges. The new model capability to estimate the contribution of the scalp blood flow shall permit incorporating this information to the regularization of the inverse problem for a cleaner reconstruction of brain hemodynamics.

Treatment of Disease: The Role of Knowledge Representation for Treatment Selection

Treatment is the care and management of a patient to combat, ameliorate, or prevent a disease, disorder, or injury.

User Modelling for Patient Tailored Virtual Rehabilitation

Intelligent rehabilitation is a novel paradigm in motor rehabilitation empowering assistive technology with artificial intelligence (AI). Central to this paradigm is adaptation, the capacity of the assistive technology to dynamically accommodate to the therapy evolving demands. This chapter overviews several existing AI solutions to implement a decision making model to provide rehabilitation tools with adaptation capabilities, and provides details of a powerful approach capable of exploiting prior knowledge for a quick start and posterior knowledge to guarantee up-to-dated informed decisions. In this solution, a Markov decision process formulates an initial policy optimal within prior knowledge; a policy which is later on allow to evolve on incoming evidence to fit new requirements. This solution ensures short training periods and exhibits convergence with therapists’ criteria. In consequence, intelligent adaptation to dynamic circumstances of the patient and therapy plan is demonstrated a feasible endeavour within a real practical timeline. This might endow assistive technology with the necessary competence to be taken home and/or reduce expert surpervision.