Federica Verdini

Smartphone based Fuzzy Logic freezing of gait detection in Parkinson's Disease

The freezing of gait (FOG) is a common and highly distressing motor symptom of patients with Parkinsons Disease (PD). Effective management of FOG is difficult given its episodic nature, heterogeneous manifestation and limited responsiveness to drug treatment. Clinicians found alternative approaches, such as rhythmic cueing. We built a smartphone-based architecture in agreement with acceptability and usability requirements which is able to gather data and information useful to detect FOG. In this work fusing together the information of freeze index, energy, cadency variation and the ratio of the derivative of the energy a novel Fuzzy Logic based algorithm is developed. Performances of the Fuzzy algorithm are compared with two other algorithms showing its capability to reduce false negative detection thus improving sensitivity and specificity.

A smartphone-based architecture to detect and quantify freezing of gait in Parkinson's disease

INTRODUCTION The freezing of gait (FOG) is a common and highly distressing motor symptom in patients with Parkinsons Disease (PD). Effective management of FOG is difficult given its episodic nature, heterogeneous manifestation and limited responsiveness to drug treatment. METHODS In order to verify the acceptance of a smartphone-based architecture and its reliability at detecting FOG in real-time, we studied 20 patients suffering from PD-related FOG. They were asked to perform video-recorded Timed Up and Go (TUG) test with and without dual-tasks while wearing the smartphone. Video and accelerometer recordings were synchronized in order to assess the reliability of the FOG detection system as compared to the judgement of the clinicians assessing the videos. The architecture uses two different algorithms, one applying the Freezing and Energy Index (Moore-Bächlin Algorithm), and the other adding information about step cadence, to algorithm 1. RESULTS A total 98 FOG events were recognized by clinicians based on video recordings, while only 7 FOG events were missed by the application. Sensitivity and specificity were 70.1% and 84.1%, respectively, for the Moore-Bächlin Algorithm, rising to 87.57% and 94.97%, respectively, for algorithm 2 (McNemar value=28.42; p=0.0073). CONCLUSION Results confirm previous data on the reliability of Moore-Bächlin Algorithm, while indicating that the evolution of this architecture can identify FOG episodes with higher sensitivity and specificity. An acceptable, reliable and easy-to-implement FOG detection system can support a better quantification of the phenomenon and hence provide data useful to ascertain the efficacy of therapeutic approaches.

Gait parameter and event estimation using smartphones

BACKGROUND AND OBJECTIVES The use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well. METHODS This work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland-Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects. RESULTS High correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count. CONCLUSION This study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications.

Smartphone based freezing of gait detection for Parkinsonian patients

We built a smartphone-based architecture to detect on line Freezing of Gait (FOG) occurrences and send acoustic signals to restore gait. Parameters used for FOG detection and FOG events are stored in a local database and periodically sent to a clinical server. We tested this solution on 18 patients.

Can the Current Mobile Technology Help for Medical Assistance? The Case of Freezing of Gait in Parkinson Disease

Parkinson’s disease (PD) affects around 1.5 % people aged 65 years. Among PD features, freezing of gait (FOG) is frequent, involving almost 70 % PD people after 10 years of disease onset, and highly disabling. Effective management of FOG is a challenge for the limited responsiveness to both drug treatment and functional neurosurgery. As “cueing on demand” is the only strategy of proven efficacy on FOG, it would be crucial to develop a portable assistive device able to release suitable cues at every time the FOG occurs during the daily living (DL) of the patient, without interfering with his/her daily activities. The current smart mobile telephony devices are in principle apt to satisfy all the above mentioned requisites in terms of technological feasibility of ambulation monitoring devices and in terms of acceptability, because of their increasing widespread diffusion. In this paper we will outline a smart-phone based architecture able to detect FOG, to produce the proper cues, and to provide information for continuous monitoring of the events. The paper will specifically consider the clinical necessity, technical feasibility, economic sustainability of the solution proposed and its potential of application.

Real-time mental stress detection based on smartwatch

In this work we propose a real-time detection of mental stress during different cognitive tasks. Stress is classified processing Galvanic Skin Response (GSR), RR Interval and Body Temperature (BT) acquired by a commercial smartwatch. The unobtrusive system proposed is validated through clinical psychological tests.

Accuracy evaluation of the Kinect v2 sensor during dynamic movements in a rehabilitation scenario

In this paper, the accuracy evaluation of the Kinect v2 sensor is investigated in a rehabilitation scenario. The accuracy analysis is provided in terms of joint positions and angles during dynamic postures used in low-back pain rehabilitation. Although other studies have focused on the validation of the accuracy in terms of joint angles and positions, they present results only considering static postures whereas the rehabilitation exercise monitoring involves to consider dynamic movements with a wide range of motion and issues related to the joints tracking. In this work, joint positions and angles represent clinical features, chosen by medical staff, used to evaluate the subjects movements. The spatial and temporal accuracy is investigated with respect to the gold standard, represented by a stereophotogrammetric system, characterized by 6 infrared cameras. The results provide salient information for evaluating the reliability of Kinect v2 sensor for dynamic postures.

A MATLAB-based graphical user interface for the identification of muscular activations from surface electromyography signals

In this paper a graphical user interface (GUI) built in MATLAB® environment is presented. This interactive tool has been developed for the analysis of superficial electromyography (sEMG) signals and in particular for the assessment of the muscle activation time intervals. After the signal import, the tool performs a first analysis in a totally user independent way, providing a reliable computation of the muscular activation sequences. Furthermore, the user has the opportunity to modify each parameter of the on/off identification algorithm implemented in the presented tool. The presence of an user-friendly GUI allows the immediate evaluation of the effects that the modification of every single parameter has on the activation intervals recognition, through the real-time updating and visualization of the muscular activation/deactivation sequences. The possibility to accept the initial signal analysis or to modify the on/off identification with respect to each considered signal, with a real-time visual feedback, makes this GUI-based tool a valuable instrument in clinical, research applications and also in an educational perspective.

A tool for home-based rehabilitation allowing for clinical evaluation in a visual markerless scenario.

This work deals with the design of an interactive monitoring tool for home-based physical rehabilitation. The software platform includes a video processing stage and the exercise performance evaluation. Image features are extracted by a Kinect v2 sensor and elaborated to return the exercises score. Furthermore the tool provides to physiotherapists a quantitative exercise evaluation of subjects performances. The proposed tool for home rehabilitation has been tested on 5 subjects and 5 different exercises and results are presented. In particular both exercises and relative evaluation indexes were selected by specialists in neurorehabilitation.

Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons

Mobile and pervasive computing enabled a new realm of possibilities into the indoor positioning domain. Although many candidate technologies have been proposed, no one can still adapt to every use case. A case centered design and the implementation of the solution within the specific domain is the current research trend. With the rise of Bluetooth Low Energy (BLE) Beacons, i.e., platforms used to interact digitally with the real world, more standard positioning solutions are emerging in different contexts. However the reachable positioning accuracy with this technology is still unacceptable for some real applications (e.g., in the healthcare sector or the emergency management). In this paper, an hybrid localization application coupling a real time model based Pedestrian Dead Reckoning (PDR) technique and the analysis of the Received Signal Strength Indicator (RSSI) of BLE beacons is proposed. In particular, the smartphone application is composed by three main real time threads: a model based step length estimation, heading determination and the fusion of beacon information to reset the position and the drift error of the PDR. In order to give soundness to our approach we firstly validated the step length smartphone app with a stereo-photogrammetric system. The whole proposed solution was then tested on fifteen healthy subjects.