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).

Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients’ daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients’ daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.

Noise pollution has a strong impact on wildlife by disrupting vocal communication or inducing physiological stress. Songbirds are particularly reliant on vocal communication as they use song during territorial and sexual interactions. Birds living in noisy environments have been shown to change the acoustic and temporal parameters of their song presumably to maximize signal transmissibility. Also, research shows that birds advance their dawn chorus in urban environments to avoid the noisiest hours, but little is known on the consequences of these changes in the time they spent singing at dawn. Here we present a comprehensive view of the European blackbird singing behaviour living next to a large airport in Madrid, using as a control a population living in a similar but silent forest. Blackbird song is composed of two parts: a series of loud low-frequency whistles (motif) and a final flourish (twitter). We found that airport blackbirds were more likely to sing songs without the twitter part. Also, when songs included a twitter part, airport blackbirds used a smaller proportion of song for the twitter than control blackbirds. Interestingly, our results show no differences in song frequency between airport and control populations. The most relevant finding of this study shows that airport blackbirds not only sang earlier but also increased the time they spent singing when chorus and aircraft traffic coincide on time. This effect disappeared as the season progressed and the chorus and the aircraft traffic day-time schedule were separated on time. We propose that the typical urban upshift in frequency might not be useful under the conditions of noise and landscape structure found near airports. We suggest that the modifications in singing behaviour induced by aircraft noise may be adaptive and that they are specific to airport acoustic habitat. Moreover, we found that adjustment of singing activity in relation to noise is plastic and possibly optimized to cope with aircraft traffic activity. In a soundscape characterised by intermittent and strong noise bursts, singing for longer could be more advantageous than modifying frequency parameters, although it is likely more costly.