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Publicações

Publicações por Hugo Miguel Choupina

2016

A novel portable, low-cost kinect-based system for motion analysis in neurological diseases

Autores
Silva Cunha, JPS; Rocha, AP; Pereira Choupina, HMP; Fernandes, JM; Rosas, MJ; Vaz, R; Achilles, F; Loesch, AM; Vollmar, C; Hartl, E; Noachtar, S;

Publicação
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Abstract
Many neurological diseases, such as Parkinson's disease and epilepsy, can significantly impair the motor function of the patients, often leading to a dramatic loss of their quality of life. Human motion analysis is regarded as fundamental towards an early diagnosis and enhanced follow-up in this type of diseases. In this contribution, we present NeuroKinect, a novel system designed for motion analysis in neurological diseases. This system includes an RGB-D camera (Microsoft Kinect) and two integrated software applications, KiT (KinecTracker) and KiMA (Kinect Motion Analyzer). The applications enable the preview, acquisition, review and management of data provided by the sensor, which are then used for motion analysis of relevant events. NeuroKinect is a portable, low-cost and markerless solution that is suitable for use in the clinical environment. Furthermore, it is able to provide quantitative support to the clinical assessment of different neurological diseases with movement impairments, as demonstrated by its usage in two different clinical routine scenarios: gait analysis in Parkinson's disease and seizure semiology analysis in epilepsy. © 2016 IEEE.

2016

EP 114. Uncovering epileptic seizures – A feasibility study for the semiological analysis of hidden patient motion during epileptic seizures

Autores
Achilles, F; Choupina, H; Loesch, A; S. Cunha, J; Remi, J; Vollmar, C; Tombari, F; Navab, N; Noachtar, S;

Publicação
Clinical Neurophysiology

Abstract

2015

Kinect v2 Based System for Parkinson's Disease Assessment

Autores
Rocha, AP; Choupina, H; Fernandes, JM; Rosas, MJ; Vaz, R; Silva Cunha, JPS;

Publicação
2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

Abstract
Human motion analysis can provide valuable information for supporting the clinical assessment of movement disorders, such as Parkinson's disease (PD). In this contribution, we study the suitability of a Kinect v2 based system for supporting PD assessment in a clinical environment, in comparison to the original Kinect (v1). In this study, 3-D body joint data were acquired from both normal subjects, and PD patients treated with deep brain stimulation (DBS). Then, several gait parameters were extracted from the gathered data. The obtained results show that 96% of the considered parameters are appropriate for distinguishing between non-PD subjects, PD patients with DBS stimulator switched on, and PD patients with stimulator switched off (p-value < 0.001, Kruskal-Wallis test). These results are markedly better than the ones obtained using Kinect v1, where only 73% of the parameters are considered appropriate (p-value < 0.001).

2016

NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification

Autores
Cunha, JPS; Choupina, HMP; Rocha, AP; Fernandes, JM; Achilles, F; Loesch, AM; Vollmar, C; Hartl, E; Noachtar, S;

Publicação
PLOS ONE

Abstract
Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure -induced body movements - called seizure semiology. Thus, synchronous EEG and (2D) video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m(3) vs. 0.14 m(3), p< 0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p< 0.01) and presented higher jerking levels (mean = 345 ms(-3) vs 172 ms(-3), p< 0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis.

2014

Parkinson's Disease Assessment Based on Gait Analysis Using an Innovative RGB-D Camera System

Autores
Rocha, AP; Choupina, H; Fernandes, JM; Rosas, MJ; Vaz, R; Silva Cunha, JPS;

Publicação
2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

Abstract
Movement-related diseases, such as Parkinson's disease (PD), progressively affect the motor function, many times leading to severe motor impairment and dramatic loss of the patients' quality of life. Human motion analysis techniques can be very useful to support clinical assessment of this type of diseases. In this contribution, we present a RGB-D camera (Microsoft Kinect) system and its evaluation for PD assessment. Based on skeleton data extracted from the gait of three PD patients treated with deep brain stimulation and three control subjects, several gait parameters were computed and analyzed, with the aim of discriminating between non-PD and PD subjects, as well as between two PD states (stimulator ON and OFF). We verified that among the several quantitative gait parameters, the variance of the center shoulder velocity presented the highest discriminative power to distinguish between non-PD, PD ON and PD OFF states (p = 0.004). Furthermore, we have shown that our low-cost portable system can be easily mounted in any hospital environment for evaluating patients' gait. These results demonstrate the potential of using a RGB-D camera as a PD assessment tool.

2017

The first Transthyretin Familial Amyloid Polyneuropathy gait quantification study - Preliminary results

Autores
Vilas Boas, MD; Rocha, AP; Pereira Choupina, HMP; Fernandes, JM; Coelho, T; Silva Cunha, JPS;

Publicação
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a rare neurological disease caused by a genetic mutation with a variable presentation and consequent challenging diagnosis, complex follow-up and treatment. At this moment, this condition has no cure and treatment options are under development. One of the disease's implications is a definite and progressive motor impairment that from the early stages compromises walking ability and daily life activities. The detection of this impairment is key for the disease onset diagnosis. With the goal of improving diagnosis of the symptoms and patients' quality of life, the authors have assessed the gait characteristics of subjects suffering from this condition. This contribution shows the results of a preliminary study, using a non-intrusive, markerless vision-based gait analysis tool. To the best of our knowledge, the reported results constitute the first gait analysis data of TTR-FAP mutation carriers. © 2017 IEEE.

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