Monipar: movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones.

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms. Objective: This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation. To implement this framework, an m-health tool named Monipar was developed that uses off-the-shelf smart devices. Methods: An experimental protocol was conducted with the participation of 21 early-stage PD patients and 7 healthy controls who used Monipar installed in off-the-shelf smartwatches and smartphones. Movement data collected using the built-in acceleration sensors were used to extract relevant digital indicators (features). These indicators were then compared with clinical evaluations performed using the MDS-UPDRS scale. Results: The results showed moderate to strong (significant) correlations between the clinical evaluations (MDS-UPDRS scale) and features extracted from the movement data used to assess resting tremor (i.e., the standard deviation of the time series: r = 0.772, p < 0.001) and data from the pronation and supination movements (i.e., power in the band of 1-4 Hz: r = -0.662, p < 0.001). Conclusion: These results suggest that the proposed framework could be used as a complementary tool for the evaluation of motor symptoms in early-stage PD patients, providing a feasible and cost-effective solution for remote and ambulatory monitoring of specific motor symptoms such as resting tremor or bradykinesia.

Preclinical examination of early-onset thalamic-cortical seizures after hemispheric stroke.

OBJECTIVE: Ischemic stroke is one of the main causes of death and disability worldwide and currently has limited treatment options. Electroencephalography (EEG) signals are significantly affected in stroke patients during the acute stage. In this study, we preclinically characterized the brain electrical rhythms and seizure activity during the hyperacute and late acute phases in a hemispheric stroke model with no reperfusion. METHODS: EEG signals and seizures were studied in a model of hemispheric infarction induced by permanent occlusion of the middle cerebral artery (pMCAO), which mimics the clinical condition of stroke patients with permanent ischemia. Electrical brain activity was also examined using a photothrombotic (PT) stroke model. In the PT model, we induced a similar (PT group-1) or smaller (PT group-2) cortical lesion than in the pMCAO model. For all models, we used a nonconsanguineous mouse strain that mimics human diversity and genetic variation. RESULTS: The pMCAO hemispheric stroke model exhibited thalamic-origin nonconvulsive seizures during the hyperacute stage that propagated to the thalamus and cortex. The seizures were also accompanied by progressive slowing of the EEG signal during the acute phase, with elevated delta/theta, delta/alpha, and delta/beta ratios. Cortical seizures were also confirmed in the PT stroke model of similar lesions as in the pMCAO model, but not in the PT model of smaller injuries. SIGNIFICANCE: In the clinically relevant pMCAO model, poststroke seizures and EEG abnormalities were inferred from recordings of the contralateral hemisphere (noninfarcted hemisphere), emphasizing the reciprocity of interhemispheric connections and that injuries affecting one hemisphere had consequences for the other. Our results recapitulate many of the EEG signal hallmarks seen in stroke patients, thereby validating this specific mouse model for the examination of the mechanistic aspects of brain function and for the exploration of the reversion or suppression of EEG abnormalities in response to neuroprotective and anti-epileptic therapies.

First Longitudinal Study Using Binaural Beats on Parkinson Disease.

This paper describes a longitudinal study to analyze the effects of acoustic stimulation with Binaural Beats (BBs) at 14[Formula: see text]Hz (beta band) in patients with Parkinson's Disease (PD). Participants ([Formula: see text], age [Formula: see text], stage [Formula: see text] Hoehn and Yahr scale) listened to binaural stimulation for 10[Formula: see text]min a day, 3 days a week, during six months and were assessed 3 times during this period using electroencephalography (EEG), cognitive (PD-CRS), quality of life (PDQ-39) and wearing-off (WOQ-19) tests. During each assessment (basal, and after 3 and 6 months), the relative power in theta band was analyzed before, during and after the stimulation. Focusing the analysis on the motor cortex, the results obtained have confirmed the initial hypothesis for the first session, but they have shown a habituation effect which decreases its efficiency with time. Also, different reactions have been detected among individuals, with some reacting as expected from the beginning, while others would react in an opposite way at the beginning but they have shown afterwards a tendency towards the expected outcome. Anyhow, the relative power of the theta band was reduced between the first and the last session for more than half of the participants, although with very different values. Subtle changes have also been observed in some items of the PD-CRS, PDQ-39 and WOQ-19 tests.

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering.

The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.

How the COVID-19 Pandemic Muted and Remixed the World's Acoustics for a While.

Purpose of Review: This review aims to analyze the effects of the pandemic on the world's sound environment. Recent Findings: The confinements associated with the pandemic led to a reduction in sound levels worldwide and a change in the perception of soundscapes in the absence of traffic noise and human-generated noise. Summary: In response to the COVID-19 pandemic, many countries and regions around the world adopted a series of interventions in 2020 that have been referred to as "lockdown" or "confinement." These sets of restrictions had a clear and obvious consequence derived from the absence of people in the streets and the reduction of daily activity and commuting, which caused an unprecedented silencing on a large scale. Along with the silence that ensued, the pandemic and the confinements affected acoustics and our relationship with sounds on different scales. In the cities, this phenomenon had a strong reduction in acoustic intensity due to the absence of vehicles on the streets. Perhaps this was more perceptible in our neighborhoods, with notable changes in their soundscapes, first due to the absence of people in the streets and later due to more outdoor activity derived from the fear of the spread of the virus in indoor spaces. The longer periods of time spent in our homes during the lockdowns also highlighted the importance of sound insulation in buildings and the acoustic conditioning of our schools or homes.

Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling.

This paper presents the implementation of a measurement system that uses a four microphone array and a data-driven algorithm to estimate depth of cut during end milling operations. The audible range acoustic emission signals captured with the microphones are combined using a spectral subtraction and a blind source separation algorithm to reduce the impact of noise and reverberation. Afterwards, a set of features are extracted from these signals which are finally fed into a nonlinear regression algorithm assisted by machine learning techniques for the contactless monitoring of the milling process. The main advantages of this algorithm lie in relatively simple implementation and good accuracy in its results, which reduce the variance of the current noncontact monitoring systems. To validate this method, the results have been compared with the values obtained with a precision dynamometer and a geometric model algorithm obtaining a mean error of 1% while maintaining an STD below 0.2 mm.

Sleep-Wake Cycle and EEG-Based Biomarkers during Late Neonate to Adult Transition.

During the transition from neonate to adulthood, brain maturation establishes coherence between behavioral states-wakefulness, non-rapid eye movement, and rapid eye movement sleep. In animal models few studies have characterized and analyzed cerebral rhythms and the sleep-wake cycle in early ages, in relation to adulthood. Since the analysis of sleep in early ages can be used as a predictive model of brain development and the subsequent emergence of neural disturbances in adults, we performed a study on late neonatal mice, an age not previously characterized. We acquired longitudinal 24 h electroencephalogram and electromyogram recordings and performed time and spectral analyses. We compared both age groups and found that late neonates: (i) spent more time in wakefulness and less time in non-rapid eye movement sleep, (ii) showed an increased relative band power in delta, which, however, reduced in theta during each behavioral state, (iii) showed a reduced relative band power in beta during wakefulness and non-rapid eye movement sleep, and (iv) manifested an increased total power over all frequencies. The data presented here might have implications expanding our knowledge of cerebral rhythms in early ages for identification of potential biomarkers in preclinical models of neurodegeneration.

Changes in noise levels in the city of Madrid during COVID-19 lockdown in 2020.

The lockdown that Madrid has suffered during the months of March to June 2020 to try to control and minimize the spread of COVID-19 has significantly altered the acoustic environment of the city. The absence of vehicles and people on the streets has led to a noise reduction captured by the monitoring network of the City of Madrid. In this article, an analysis has been carried out to describe the reduction in noise pollution that has occurred and to analyze the changes in the temporal patterns of noise, which are strongly correlated with the adaptation of the population's activity and behavior to the new circumstances. The reduction in the sound level ranged from 4 to 6 dBA for the indicators Ld, Le, and Ln, and this is connected to a significant variation in the daily time patterns, especially during weekends, when the activity started earlier in the morning and lasted longer at midday, decreasing significantly in the afternoon.

A Taxonomy Proposal for the Assessment of the Changes in Soundscape Resulting from the COVID-19 Lockdown.

Many countries around the world have chosen lockdown and restrictions on people's mobility as the main strategies to combat the COVID-19 pandemic. These actions have significantly affected environmental noise and modified urban soundscapes, opening up an unprecedented opportunity for research in the field. In order to enable these investigations to be carried out in a more harmonized and consistent manner, this paper makes a proposal for a set of indicators that will enable to address the challenge from a number of different approaches. It proposes a minimum set of basic energetic indicators, and the taxonomy that will allow their communication and reporting. In addition, an extended set of descriptors is outlined which better enables the application of more novel approaches to the evaluation of the effect of this new soundscape on people's subjective perception.

Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors.

Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson's disease (PD). The occurrence of FOG reduces the patients' quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms' evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients' homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).

A Digital Signal Processor Based Acoustic Sensor for Outdoor Noise Monitoring in Smart Cities.

Presently, large cities have significant problems with noise pollution due to human activity. Transportation, economic activities, and leisure activities have an important impact on noise pollution. Acoustic noise monitoring must be done with equipment of high quality. Thus, long-term noise monitoring is a high-cost activity for administrations. For this reason, new alternative technological solutions are being used to reduce the costs of measurement instruments. This article presents a design for a versatile electronic device to measure outdoor noise. This device has been designed according to the technical standards for this type of instrument, which impose strict requirements on both the design and the quality of the device's measurements. This instrument has been designed under the original equipment manufacturer (OEM) concept, so the microphone-electronics set can be used as a sensor that can be connected to any microprocessor-based device, and therefore can be easily attached to a monitoring network. To validate the instrument's design, the device has been tested following the regulations of the calibration laboratories for sound level meters (SLM). These tests allowed us to evaluate the behavior of the electronics and the microphone, obtaining different results for these two elements. The results show that the electronics and algorithms implemented fully fit within the requirements of type 1 noise measurement instruments. However, the use of an electret microphone reduces the technical features of the designed instrument, which can only fully fit the requirements of type 2 noise measurement instruments. This situation shows that the microphone is a key element in this kind of instrument and an important element in the overall price. To test the instrument's quality and show how it can be used for monitoring noise in smart wireless acoustic sensor networks, the designed equipment was connected to a commercial microprocessor board and inserted into the infrastructure of an existing outdoor monitoring network. This allowed us to deploy a low-cost sub-network in the city of Málaga (Spain) to analyze the noise of conflict areas due to high levels of leisure noise. The results obtained with this equipment are also shown. It has been verified that this equipment meets the similar requirements to those obtained for type 2 instruments for measuring outdoor noise. The designed equipment is a two-channel instrument, that simultaneously measures, in real time, 86 sound noise parameters for each channel, such as the equivalent continuous sound level (Leq) (with Z, C, and A frequency weighting), the peak level (with Z, C, and A frequency weighting), the maximum and minimum levels (with Z, C, and A frequency weighting), and the impulse, fast, and slow time weighting; seven percentiles (1%, 5%, 10%, 50%, 90%, 95%, and 99%); as well as continuous equivalent sound pressure levels in the one-third octave and octave frequency bands.

Beyond sound level monitoring: Exploitation of social media to gather citizens subjective response to noise.

Subjective response to noise is probably the most important goal in environmental acoustics. Traditional surveys have the drawback of high cost deriving from its development and execution, the limited number of participants, and the duration of the surveying campaign. The main drawbacks of online surveys are the low participation, or the self-produced bias that concerns about the topic can raise. In both cases, the process of designing questionnaires, implementing the survey, and analysing the results can be long, expensive and ineffective to monitor changes in the short-term. With the creation of Online Social Networks (OSN), people have changed the manner they communicate and use technology. Nowadays, people can provide information regarding their likes, opinion and discomfort about any topic, including noise, in just a few minutes with their smartphone. These Internet opinions can be analysed automatically using machine learning and Natural Language Processing techniques to get insights that can help in the early detection of noise problems, or in the prior assessment of action plans. This information can be significant helpful in addressing noise management by local authorities and stakeholders. The purpose of this paper is to present a novel methodology, based on machine learning, allowing for the gathering and processing of OSN text data, enabling the generation of a system able to exploit the data to detect noise complaints and to classify them by source. This methodology has been piloted in a case study using Twitter, and the main results are presented.

Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer.

Wearable technology has had a significant growth in the last years; this is particularly true of smartwatches, due to their potential advantages and ease of use. These smart devices integrate sensors that can be potentially used within industrial settings and for several applications, such as safety, monitoring, and the identification of occupational risks. The accelerometer is one of the main sensors integrated into these devices. However, several studies have identified that sensors integrated into smart devices may present inaccuracies during data acquisition, which may influence the performance of their potential applications. This article presents an analysis from the metrological point of view to characterize the amplitude and frequency response of the integrated accelerometers in three currently available commercial smartwatches, and it also includes an analysis of the uncertainties associated with these measurements by adapting the procedures described in several International Organization for Standardization (ISO) standards. The results show that despite the technical limitations produced by the factory configuration, these devices can be used in various applications related to occupational risk assessment. Opportunities for improvement have also been identified, which will allow us to take advantage of this technology in several innovative applications within industrial settings and, in particular, for occupational health purposes.

Communicating airport noise emission data to the general public.

Despite the efforts that the aviation industry has undertaken during the last few decades, noise annoyance remains high, partly because of the continuous transport demands of modern societies and partly because of changes in citizen expectations and their growing environmental concerns. Although modern aircraft are considerably quieter than their predecessors, the number of complaints has not decreased as much as expected. Therefore, the aeronautical sector has tried more sociological and/or psychological strategies to gain acceptance through awareness and community engagement. In this regard, noise communication to the public is crucial for managers and policy makers. Noise information is a difficult technical topic for non-experts, which is an issue that must first be addressed to take advantage of the new possibilities that have recently been opened by the internet and information and communication technologies. In this review paper, we have compiled the literature that shows the increasing importance of communicating noise information from aircraft and the variety of indicators used to communicate with the public. We also examined the methods of representing noise data, using visualization strategies, and new tools airports are currently using to address this communication problem.