Mobile navigation apps are among the most used mobile applications and are often used as a baseline to evaluate new mobile navigation technologies in field studies. As field studies often introduce external factors that are hard to control for, we investigate how pedestrian navigation methods can be evaluated in virtual reality (VR). We present a study comparing navigation methods in real life (RL) and VR to evaluate if VR environments are a viable alternative to RL environments when it comes to testing these. In a series of studies, participants navigated a real and a virtual environment using a paper map and a navigation app on a smartphone. We measured the differences in navigation performance, task load and spatial knowledge acquisition between RL and VR.From these we formulate guidelines for the improvement of pedestrian navigation systems in VR like improved legibility for small screen devices. We furthermore discuss appropriate low-cost and low-space VR-locomotion techniques and discuss more controllable locomotion techniques.

Background Parkinson disease (PD) is one of the most common neurological diseases. At present, because the exact cause is still unclear, accurate diagnosis and progression monitoring remain challenging. In recent years, exploring the relationship between PD and speech impairment has attracted widespread attention in the academic world. Most of the studies successfully validated the effectiveness of some vocal features. Moreover, the noninvasive nature of speech signal–based testing has pioneered a new way for telediagnosis and telemonitoring. In particular, there is an increasing demand for artificial intelligence–powered tools in the digital health era. Objective This study aimed to build a real-time speech signal analysis tool for PD diagnosis and severity assessment. Further, the underlying system should be flexible enough to integrate any machine learning or deep learning algorithm. Methods At its core, the system we built consists of two parts: (1) speech signal processing: both traditional and novel speech signal processing technologies have been employed for feature engineering, which can automatically extract a few linear and nonlinear dysphonia features, and (2) application of machine learning algorithms: some classical regression and classification algorithms from the machine learning field have been tested; we then chose the most efficient algorithms and relevant features. Results Experimental results showed that our system had an outstanding ability to both diagnose and assess severity of PD. By using both linear and nonlinear dysphonia features, the accuracy reached 88.74% and recall reached 97.03% in the diagnosis task. Meanwhile, mean absolute error was 3.7699 in the assessment task. The system has already been deployed within a mobile app called No Pa. Conclusions This study performed diagnosis and severity assessment of PD from the perspective of speech order detection. The efficiency and effectiveness of the algorithms indirectly validated the practicality of the system. In particular, the system reflects the necessity of a publicly accessible PD diagnosis and assessment system that can perform telediagnosis and telemonitoring of PD. This system can also optimize doctors’ decision-making processes regarding treatments.

Background Anti-N-methyl-d-aspartate (NMDA) receptor encephalitis is an autoimmune disorder characterized by complex neuropsychiatric syndromes during disease onset. Although this disease has been well documented in the last decade, clinical characteristics of anti-NMDA receptor encephalitis in patients with long-term diagnostic history of mental disorders remain unclear. Methods Here, we reviewed and analyzed series of anti-NMDA receptor encephalitis patients with a long-term medical history of psychiatric disorders through a review of literature using PubMed, web of science and Embase database. In addition, we described a patient of anti-NMDA receptor encephalitis with a long-term history of major depressive disorder. Results A total of 14 patients with anti-NMDA receptor encephalitis and a long-term history of mental disorders were included in our study. We found that most patients were adult (92.9%) and female (78.6%). These patients often first visited a psychiatric department (71.43%). The mean disease course of psychiatric disorders was more than 9 years. Speech impairment (71.4%), abnormal behaviors (64.3%), and catatonia (64.3%) were the most common clinical symptoms. Most patients (85.7%) had a satisfactory prognosis after immunotherapy. Conclusion Anti-NMDA receptor encephalitis in individuals with mental disorders is an underestimated condition, yet it presents complex clinical symptoms. Mental and behavioral impairments are more frequently observed in newly diagnosed anti-NMDA receptor encephalitis patients with a long-term history of mental disorders than those without mental illness. A diagnosis of anti-NMDA receptor encephalitis should be considered when patients with mental illness show sudden fluctuations in psychiatric symptoms.

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In order to resolve the problems of the repeated logon from various kinds of Application which in different domains, a unified identity authentication and unified access control model as shown in Fig2 is proposed, which mainly used the key technology like cookie, mobile agent and load balancing. This system had implemented corresponding function such as cross-domain single sign-on, user authentication and unified access control. Meanwhile, the security of this model was well protected since the related encryption technology was adopted, and performance of the system had greatly improved since the mobile agent technology was adopted in the rights management module. The results show that this architecture could maximize the efficiency of user access to application resources.