Analysis of the innovation outputs in mHealth for patient monitoring

Abstract—In the last decade, mobile health (mHealth) has developed as a natural consequence of the advances in mobile technologies, the growing spread of mobile devices, and their application in the provision of novel health services. mHealth has demonstrated the potential to make the health care sector more efficient and sustainable and to increase the healthcare quality. Considering the boost to the healthcare area which will be provided by mHealth, many organizations and governments have engaged in innovating in this area. In this context, this work investigated the role of innovation in the area of mHealth for patient monitoring in order to determine the trends and the performance of the innovation activities in this domain. Proxy indicators, like intellectual property statistics and scientific publication statistics, were utilized to measure the outputs of innovation during the period of time from 2006 to 2015 in Europe. Two studies were performed to provide quantitative measures for the indicators measuring innovation outputs in the domain of mHealth for patient monitoring and three main conclusions were observed. First, even if there was a lot of research in Europe in mHealth for patient monitoring, the vast majority of the enterprises did not protect their inventions. Second, a strong research collaboration in the area of mHealth for patient monitoring took place between researchers affiliated to institu- tions of different European countries and even with researchers working in Asian or American institutions. Finally, an increasing trend on the number of published articles about mHealth for patient monitoring was identified. Therefore, the findings of the studies demonstrated the great interest that has arisen the field of mHealth and the huge involvement in innovation activities in the area of mHealth for patient monitoring

First approach to automatic performance status evaluation and physical activity recognition in cancer patients

The evaluation of cancer patients’ recovery is still under the big subjectivity of physicians. Many different systems have been successfully implemented for physical activity evaluation, nonetheless there is still a big leap into Performance Status evaluation with ECOG and Karnofsky’s Performance Status scores. An automatic system for data recovering based on Android smartphone and wearables has been developed. A gamification implementation has been designed for increasing patients’ motivation in their recovery. Furthermore, novel and without-precedent algorithms for Performance Status (PS) and Physical Activity (PA) assessment have been developed to help oncologists in their diagnoses

Sistema automático de captura de movimiento en 2D para evaluación del riesgo de lesión de rodilla

La medida de los ángulos articulares del ser humano es frecuentemente utilizada como indicador de riesgo de lesión, especialmente en los miembros inferiores. Comúnmente se hace uso de la proyección bidimensional de estos ángulos como estimador de estas medidas. Sin embargo, los sistemas tradicionales de medida requieren un largo tiempo de análisis offline. En este artículo se presenta un sistema de captura y análisis en tiempo real de los ángulos articulares en 2D haciendo uso del sensor infrarrojo incluido en la cámara Kinect V2 y marcadores retro-reflectantes. El sensor captura la posición de los marcadores reflectantes y la información registrada es procesada en tiempo real por un software que proporciona la medida del ángulo articular deseado. La fiabilidad del sistema ha sido validada frente a los procedimientos tradicionales de análisis offline, obteniendo excelentes resultados

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased

Blastic plasmacytoid dendritic cell neoplasm frequently shows occult central nervous system involvement at diagnosis and benefits from intrathecal therapy

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare aggressive myeloid neoplasm which shows a high rate of central nervous system (CNS) recurrence and overall survival (OS) of <1 year. Despite this, screening for CNS involvement is not routinely performed at diagnosis and intrathecal (IT) prophylaxis is not regularly administered in BPDCN. Here, we prospectively evaluated 13 consecutive BPDCN patients for the presence of CNS involvement by flow cytometry. Despite none of the patients presented with neurological symptoms, occult CNS involvement was detected in 6/10 cases evaluated at diagnosis and 3/3 studied at relapse/progression. BPDCN patients evaluated at diagnosis received IT treatment -either CNS prophylaxis (n = 4) or active therapy (n = 6)- and all but one remain alive (median follow-up of 20 months). In contrast, all three patients assessed at relapse/progression died. The potential benefit of IT treatment administered early at diagnosis on OS and CNS recurrence-free survival of BPDCN was further confirmed in a retrospective cohort of another 23 BPDCN patients. Our results show that BPDCN patients studied at diagnosis frequently display occult CNS involvement; moreover, they also indicate that treatment of occult CNS disease might lead to a dramatically improved outcome of BPDCN

Dealing with the effects of sensor displacement in wearable activity recognition

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.This work was supported by the High Performance Computing (HPC)-Europa2 project funded by the European Commission-DG Research in the Seventh Framework Programme under grant agreement No. 228398 and by the EU Marie Curie Network iCareNet under grant No. 264738. This work was also supported by the Spanish Comision Interministerial de Ciencia y Tecnologia (CICYT) Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476 and the FPU Spanish grant, AP2009-2244

SPIRA: an automatic system to support lower limb injury assessment

Lower limb injuries, especially those related to the knee joint, are some of the most common and severe injuries among sport practitioners. Consequently, a growing interest in the identification of subjects with high risk of injury has emerged during last years. One of the most commonly used injury risk factor is the measurement of joint angles during the execution of dynamic movements. To that end, techniques such as human motion capture and video analysis have been widely used. However, traditional procedures to measure joint angles present certain limitations, which makes this practice not practical in common clinical settings. This work presents SPIRA, a novel 2D video analysis system directed to support practitioners during the evaluation of joint angles in functional tests. The system employs an infrared camera to track retro-reflective markers attached to the patient’s body joints and provide a real-time measurement of the joint angles in a cost-and-time-effective way. The information gathered by the sensor is processed and managed through a computer application that guides the expert during the execution of the tests and expedites the analysis of the results. In order to show the potential of the SPIRA system, a case study has been conducted, performing the analysis with the both the proposed system and a gold-standard in 2D offline video analysis. The results (ICC(ρ) = 0.996) reveal a good agreement between both tools and prove the reliability of SPIRA