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 Parkinson’s 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

Stress detection in computer users from keyboard and mouse dynamics

Detecting stress in computer users, while technically challenging, is of the utmost importance in the workplace, especially now that remote working scenarios are becoming ubiquitous. In this context, cost-effective, subject-independent systems are needed that can be embedded in consumer devices and classify users' stress in a reliable and unobtrusive fashion. Leveraging keyboard and mouse dynamics is particularly appealing in this context as it exploits readily available sensors. However, available studies are mostly performed in laboratory conditions, and there is a lack of on-field investigations in closer-to-real-world settings. In this study, keyboard and mouse data from 62 volunteers were experimentally collected in-the-wild using a purpose-built Web application, designed to induce stress by asking each subject to perform 8 computer tasks under different stressful conditions. The application of Multiple Instance Learning (MIL) to Random Forest (RF) classification allowed the devised system to successfully distinguish 3 stress-level classes from keyboard (76% accuracy) and mouse (63% accuracy) data. Classifiers were further evaluated via confusion matrix, precision, recall, and F1-score

Development of a system to manage motor disorders in Parkinson's disease

Il Parkinson è una malattia neurodegenerativa associata alla comparsa di disordini motori. Il Freezing del Cammino (FdC) è un blocco del cammino che si manifesta come un’improvvisa e transitoria inabilità di progredire in avanti nonostante l’intenzione. Due sono i maggiori problemi associati al FdC: scarsa conoscenza clinica e resistenza alla terapia farmacologica. La terapia riabilitativa può alleviare il FdC, come l’uso di stimoli acustici ritmici (cues). Scopo di questa tesi è lo sviluppo di un sistema di gestione del FdC che consenta di affrontare i due problemi sopra elencati. I requisiti di usabilità e accettabilità hanno guidato la progettazione. Il sistema proposto si compone di una parte mobile a una su server. La prima è un’applicazione smartphone capace di monitorare il passo, riconoscere episodi di FdC, fornire cue per aiutare il paziente a superare il blocco, immagazzinare le informazioni raccolte ed inviarle al server. La parte su server è composta da un database, per immagazzinare le informazioni cliniche e di monitoraggio del paziente, e un’applicazione web che consenta ai clinici una consultazione semplice ed efficace dei dati relativi al FdC. In tal modo, i clinici potranno usare queste informazioni per migliorare la loro conoscenza del fenomeno e personalizzare le terapie riabilitativa e farmacologica. L’affidabilità dello smartphone nel monitoraggio del cammino è stata valutata su soggetti sani tramite confronto con un sistema stereofotogrammetrico. Relativamente al monitoraggio del FdC, l’architettura è stata testata su 60 pazienti raggiungendo alti valori di sensibilità, specificità, accuratezza e precisione. Usabilià e accettabilità sono state valutate tramite questionari, che hanno rivelato l’intenzione dei pazienti ad usare il sistema. I risultati ottenuti evidenziano le potenzialità dell’architettura di essere applicata nella vita reale, migliorando la qualità di vita e la gestione del FdC.Increase in longevity caused an increase in age related diseases, with a high impact on elderly quality of life. Parkinson’s Disease is a neurodegenerative disorder associated with the advent of motor symptoms. Among these, Freezing of Gait (FoG) is a common and highly distressing motor block that manifests as a transient inability to move forward despite the intention to walk. Two main problems are associated with FoG: lack of clinical knowledge and resistance to pharmacological treatment. Rehabilitation therapy can alleviate FoG, in fact evidences showed that the delivery of rhythmic stimuli (cues) helps patients to overcome the block. Given this background, the thesis aims to develop a system to manage FoG in order to face the above problems. Usability and acceptability requirements led the design process. The proposed system is composed of a mobile part and a server part. The first one consists of a smartphone app able to monitor the gait, detect FoG episodes, provide cues, store collected information and send this data to the remote server. The server side part is composed of a database to store monitoring and clinical information about patients and a web application to allow an effective and accessible consultation of FoG monitoring data by clinicians, who can use this information to improve their knowledge and customize patients’ and pharmacological and rehabilitation therapies. Smartphone reliability in monitoring gait parameters was tested on healthy subjects, through comparison with stereophotogrammetry, which is the gold standard for gait analysis. The architecture was tested on 60 patients in FoG monitoring, reaching high scores of sensitivity, specificity, accuracy and precision. Usability and acceptability was also assessed through questionnaires, revealing patients’ attitude to use the system. The obtained results suggest the architecture potential to be applied in a daily living scenario, thus improving patients’ quality of life and clinical management of FoG

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-bystep 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