Small, compact and embedded sensors are a pervasive technology in everyday life for a wide number of applications (e.g., wearable devices, domotics, e-health systems, etc.). In this context, wireless transmission plays a key role, and among available solutions, Bluetooth Low Energy (BLE) is gaining more and more popularity. BLE merges together good performance, low-energy consumption and widespread diffusion. The aim of this work is to review the main methodologies adopted to investigate BLE performance. The first part of this review is an in-depth description of the protocol, highlighting the main characteristics and implementation details. The second part reviews the state of the art on BLE characteristics and performance. In particular, we analyze throughput, maximum number of connectable sensors, power consumption, latency and maximum reachable range, with the aim to identify what are the current limits of BLE technology. The main results can be resumed as follows: throughput may theoretically reach the limit of ~230 kbps, but actual applications analyzed in this review show throughputs limited to ~100 kbps; the maximum reachable range is strictly dependent on the radio power, and it goes up to a few tens of meters; the maximum number of nodes in the network depends on connection parameters, on the network architecture and specific device characteristics, but it is usually lower than 10; power consumption and latency are largely modeled and analyzed and are strictly dependent on a huge number of parameters. Most of these characteristics are based on analytical models, but there is a need for rigorous experimental evaluations to understand the actual limits.

Background: In the last decades, several studies showed that wearable sensors, used for assessing Parkinson’s disease (PD) motor symptoms and recording their fluctuations, could provide a quantitative and reliable tool for patient’s motor performance monitoring. Objective: The aim of this study is to make a step forward the capability of quantitatively describing PD motor symptoms. The specific aims are: identify the most sensible place where to locate sensors to monitor PD bradykinesia and rigidity, and identify objective indexes able to discriminate PD OFF/ON motor status, and PD patients from healthy subjects (HSs). Methods: Fourteen PD patients (H&Y stage 1–2.5), and 13 age-matched HSs, were enrolled. Five magneto-inertial wearable sensors, placed on index finger, thumb, metacarpus, wrist, and arm, were used as motion tracking systems. Sensors were placed on the most affected arm of PD patients, and on dominant hand of HS. Three UPDRS part III tasks were evaluated: rigidity (task 22), finger tapping (task 23), and prono-supination movements of the hands (task 25). A movement disorders expert rated the three tasks according to the UPDRS part III scoring system. In order to describe each task, different kinematic indexes from sensors were extracted and analyzed. Results: Four kinematic indexes were extracted: fatigability; total time; total power; smoothness. The last three well-described PD OFF/ON motor status, during finger-tapping task, with an index finger sensor. During prono-supination task, wrist sensor was able to differentiate PD OFF/ON motor condition. Smoothness index, used as a rigidity descriptor, provided a good discrimination of the PD OFF/ON motor status. Total power index, showed the best accuracy for PD vs healthy discrimination, with any sensor location among index finger, thumb, metacarpus, and wrist. Conclusion: The present study shows that, in order to better describe the kinematic features of Parkinsonian movements, wearable sensors should be placed on a distal location on upper limb, on index finger or wrist. The proposed indexes demonstrated a good correlation with clinical scores, thus providing a quantitative tool for research purposes in future studies in this field.

Parkinson's disease (PD) is a neurodegenerative brain disorder that slowly brings on the dopaminergic neurons death. The depletion of the dopaminergic signal causes the onset of motor symptoms such as tremor, bradykinesia and rigidity. Usually, neurologists regularly monitor motor symptoms and motor fluctuations using the MDS-UPDRS part III clinical scale. Nevertheless, to have a more objective and quantitative evaluation, it is possible to assess the cardinal motor symptoms of PD using wearable sensors and portable robotic devices. Unfortunately while there are several research papers on the use of these devices on PD patients, their use is not so common in clinical practice. In this work we recorded specific MDS-UPDRS motor tasks using magneto-inertial devices, worn by seven PD subjects and seven age-matched controls, in order to deeply analyze the kinematic and dynamic characteristics of goal-directed movements of upper limb, in addition to extract quantitative indices (peak velocity, smoothness, etc) useful for the assessment of motor symptoms. Using only gyroscope signals we looked at those parameters useful to assess bradykinesia. We observed parameters changes from OFF to ON phase congruent with the MDS-UPDRS changes, especially in the frequency domain. Our results suggest the prono-supination task is the more consistent to describe the bradykinesia symptom with the gyroscopes. Probably because of the amplitude of the movement performed. Moreover the peak power looks appropriate for bradykinesia symptom evaluation. We can conclude that, similar to the studies in which tremor symptom is evaluated, it is possible to monitor the bradykinesia using few wearable sensors and few simple parameters.

In the world of smart cities, domotics, wearable sensors and smart devices, an important role is played by the communication technology involved in the data transmission between nodes of the sensor network. Among a wide set of standard wireless technologies already available on the market, Bluetooth Low Energy (BLE) is becoming one of the most widespread and exploited. The aim of this work is to propose a methodology for analyzing and characterizing throughput performance in general sensor network applications. In particular, we want to assess the effective BLE performance in order to investigate its possible usage in applications that require high throughput, such as Body Area Network (BAN) for motion tracking of human movements, using magneto-inertial sensor units (M-IMUs). In addition to this, we have also studied how different nodes, with different hardware, software and firmware, can affect network performance. The results of this study show how different typologies of nodes can be better exploited dependently on the specific application they are involved in. In fact we define how to set up BLE parameters in order to push this technology to its limit. Finally, we address the specific case of using BLE as the wireless infrastructure of a Body Area Network for human motion tracking: in this scenario we show that BLE can be employed to successfully transmit M-IMU data from 5 peripheral nodes with a sampling frequency higher than 200 Hz.

This work proposes a broad analysis for the de-tection of the most relevant features for the sit-to-stand task analysis, in Parkinson's disease (PD) patients and healthysubjects (H). A group of sixteen PD patients and thirteen H subjects have been analyzed, using one magneto-inertial sensor, while the physician administers the UPDRS clinical scale. The PD group has been examined before and after thepharmacological therapy (respectively, OFF and ON phase), in order to monitor the different states of the PD, which implies changes in motor control. By calculating the features of this task, it has been possible to choose the most reliable indexes, already used in this task in order to identify differences in the score assigned through sensors. In addition to that, it has also been possible to find differences in the features' values which the clinical scale and the physician cannotidentify. Our study highlights how wearable motion sensors can detect statistically significant differences between OFF/ON phase and H subjects that the clinical evaluation can not. We conclude that our method provides a deep analysis of the sit-to-stand task with only one M-IMU, allowing to check PD patient status,providing a method for home care monitoring.