Laura Pastor-Sanz

A comprehensive motor symptom monitoring and management system: The bradykinesia case

The current work describes a methodology to automatically detect the severity of bradykinesia in motor disease patients using wireless, wearable accelerometers. This methodology was tested with cross validation through a sample of 20 Parkinsons disease patients. The assessment of methodology was carried out through some daily living activities which were detected using an activity recognition algorithm. The Unified Parkinsons Disease Rating Scale (UPDRS) severity classification of the algorithm coincides between 70 and 86% from that of a trained neurologist depending on the classifier used. These severities were calculated for 5 second segments of the signal with 50% of overlap. A bradykinesia profiler is also presented in this work. This profiler removes the overlap of the segments and calculates the confidence of the resulting events. It also calculates average severity, duration and symmetry values for those events. The profiler has been tested with a bogus dataset. Future work includes better training for the severity classifier with a larger sample and testing the profiler with real, longterm patient data in a projected pilot phase in three European hospitals.

Gait assessment in Parkinson's disease patients through a network of wearable accelerometers in unsupervised environments

Parkinsons disease (PD) predominantly alters the motor performance of the affected individuals. In particular, the loss of dopaminergic neurons compromises the speed, the automaticity and fluidity of movements. As the disease evolves, PD patients motion becomes slower and tremoric and the response to medication fluctuates along the day. In addition, the presence of involuntary movements deteriorates voluntary movement in advanced state of the disease. These changes in the motion can be detected by studying the variation of the signals recorded by accelerometers attached in the limbs and belt of the patients. The analysis of the most significant changes in these signals make possible to build an individualized motor profile of the disease, allowing doctors to personalize the medication intakes and consequently improving the response of the patient to the treatment. Several works have been done in a laboratory and supervised environments providing solid results; this work focused on the design of unsupervised method for the assessment of gait in PD patients. The development of a reliable quantitative tool for long-term monitoring of PD symptoms would allow the accurate detection of the clinical status during the different PD stages and the evaluation of motor complications. Besides, it would be very useful both for routine clinical care as well as for novel therapies testing.

Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system

The aim of this paper is to describe and present the results of the automatic detection and assessment of bradykinesia in motor disease patients using wireless, wearable accelerometers. The current work is related to a module of the PERFORM system, a FP7 project from the European Commission, that aims at providing an innovative and reliable tool, able to evaluate, monitor and manage patients suffering from Parkinsons disease. The assessment procedure was carried out through a developed C# library that detects the activities of the patient using an activity recognition algorithm and classifies the data using a Support Vector Machine trained with data coming from previous test phases. The accuracy between the output of the automatic detection and the evaluation of the clinician both expressed with the Unified Parkinsons disease Rating Scale, presents an average value of [68.3±8.9]%. A meta-analysis algorithm is used in order to improve the accuracy to an average value of [74.4±14.9]%. Future work will include a personalized training of the classifiers in order to achieve a higher level of accuracy.

Preliminary results of ON/OFF detection using an integrated system for Parkinson's disease monitoring

This paper describes the experimental set up of a system composed by a set of wearable sensors devices for the recording of the motion signals and software algorithms for the signal analysis. This system is able to automatically detect and assess the severity of bradykinesia, tremor, dyskinesia and akinesia motor symptoms. Based on the assessment of the akinesia, the ON-OFF status of the patient is determined for each moment. The assessment performed through the automatic evaluation of the akinesia is compared with the status reported by the patients in their diaries. Preliminary results with a total recording period of 32 hours with two PD patients are presented, where a good correspondence (88.2 +/- 3.7 %) was observed. Best (93.7%) and worst (87%) correlation results are illustrated, together with the analysis of the automatic assessment of the akinesia symptom leading to the status determination. The results obtained are promising, and if confirmed with further data, this automatic assessment of PD motor symptoms will lead to a better adjustment of medication dosages and timing, cost savings and an improved quality of life of the patients.

A WEARABLE EMG MONITORING SYSTEM FOR EMOTIONS ASSESSMENT

The automatic detection and study of human emotions has always been an area of interest within the scientific community. The AUBADE European Union funded project has addressed this problem by developing an innovative, intelligent, multi-sensor and wearable system for the assessment of the emotional state of humans under special conditions (i.e. neurological diseases, stress, etc.). The system recognizes the emotions after processing biomedical signals (electromyogram, electrocardiogram, respiration rate and galvanic skin response) and can be applied in diverse areas. Currently, a health care sector scenario has been considered and validated, primarily in the neurology and psychology areas, in order to contribute to get precise diagnosis and treatment procedures for patients.

Mobile Systems as a Challenge for Neurological Diseases Management – The Case of Parkinson's Disease

Nowadays the importance of bio-medical engineering and mobile applications for healthcare is amazingly growing. During the last decades many devices and technological solutions have become available on the market and the interest in applying those technologies to the treatment of several kinds of pathologies has consequently increased. This chapter addresses the problem of continuous monitoring of patients affected by Parkinson’s Disease (PD) and proposes a set of technologies to improve the following and management of such subjects. PD is a neurodegenerative disorder of the central nervous system that affects motor skills and speech (Tolosa, 1998). The primary biochemical abnormality in PD is a deficiency of dopamine due to degeneration of neurons in the substantia nigra pars compact (D. G. Standaert & Young, 2001). The characteristic motor features of the disease include bradykinesia (i.e. slowness of movement), tremor, rigidity (i.e. resistance to externally imposed movements), flexed posture, postural instability and freezing of gait. Furthermore, PD is usually characterised by the loss of normal prosody of the speech (Darkins et al., 1988). According to the World Health Organisation [WHO], 2002), there are more than six million people worldwide affected by PD. The syndrome typically appears around the age of 60. It affects Europeans and North Americans more often than Asians or Africans and it is more common in men than in women. PD affects about 2% of the population over the age of 65 years, figure that is expected to double by 2020 (de Lau & Breteler, 2006). For those reasons, PD poses a significant public health burden, which is likely to increase in the coming years. Annual medical care, including doctors’ visits, physical therapies and treatment for cooccurring illnesses -such as depressionis estimated at

Clinical validation of a wearable system for emotional recognition based on biosignals.

2,000 to

Remote Monitoring of Post-stroke Patients Suffering from Apraxia and Action Disorganisation Syndrome

7,000 for people in early stages of the disease, and it is probably much higher for advanced stages. Surgical treatments for PD can cost

2.104 AUTOMATIC ASSESSMENT OF BRADYKINESIA IN PD PATIENTS

25,000 or more. As the disease progresses, institutional care at an assisted-living facility or nursing home may be required, and the related costs can exceed

2.103 TECHNOLOGY AND PARKINSON'S DISEASE: STATE-OF-THE-ART AND TRENDS IN REMOTE MONITORING AND REHABILITATION

100,000, per person annually. Technology in general and specifically ICT might be an affordable alternative for PD’s patients’ treatment and management. The development of platforms for remote health