No abstract

Background The natural fluctuation of motor symptoms of Parkinson's disease (PD) makes judgement of any change challenging and the use of clinical scales such as the International Parkinson and Movement Disorder Society (MDS)‐UPDRS imperative. Recently developed commodity mobile communication devices, such as smartphones, could possibly be used to assess motor symptoms in PD patients in a convenient way with low cost. We provide the first report on the development and testing of stand‐alone software for mobile devices that could be used to assess both tremor and bradykinesia of PD patients. Methods We assessed motor symptoms with a custom‐made smartphone application in 14 patients and compared the results with their MDS‐UPDRS scores. Results We found significant correlation between five subscores of MDS‐UPDRS (rest tremor, postural tremor, pronation‐supination, leg agility, and finger tapping) and eight parameters of the data collected with the smartphone. Conclusions These results provide evidence as a proof of principle that smartphones could be a useful tool to objectively assess motor symptoms in PD in clinical and experimental settings.

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

The Internet of Things (IoT) is a computing paradigm whereby everyday life objects are augmented with computational and wireless communication capabilities, typically through the incorporation of resource-constrained devices including sensors and actuators, which enable their connection to the Internet. The IoT is seen as the key ingredient for the development of smart environments. Nevertheless, the current IoT ecosystem offers many alternative communication solutions with diverse performance characteristics. This situation presents a major challenge to identifying the most suitable IoT communication solution(s) for a particular smart environment. In this paper we consider the distinct requirements of key smart environments, namely the smart home, smart health, smart cities and smart factories, and relate them to current IoT communication solutions. Specifically, we describe the core characteristics of these smart environments and then proceed to provide a comprehensive survey of relevant IoT communication technologies and architectures. We conclude with our reflections on the crucial features of IoT solutions in this setting and a discussion of challenges that remain open for research.

No abstract

rfid has already found its way into a variety of large scale applications and arguably it is already one of the most successful technologies in the history of computing. Beyond doubt, rfid is an effective automatic identification technology for a variety of objects including natural, manufactured and handmade artifacts; humans and other species; locations; and increasingly media content and mobile services. In this survey we consider developments towards establishing rfid as the cost-effective technical solution for the development of open, shared, universal pervasive computing infrastructures and look ahead to its future. In particular, we discuss the ingredients of current large scale applications; the role of network services to provide complete systems; privacy and security implications; and how rfid is helping prototype emerging pervasive computing applications. We conclude by identifying common trends in the new applications of rfid and ask questions related to sustainable universal deployment of this technology.

Abstract-The performance of wireless sensor networks (WSN) is prone to adverse influences from a number of factors such as the interference from co-located wireless systems utilising the same spectral space. Channel hopping technique was proposed to mitigate the problem via periodic change of the operating frequency, and has been adopted in the form of time slotted channel hopping (TSCH) by IEEE 802.15.4e standard. This paper proposes adaptive slotted channel hopping (A-TSCH), an enhanced version of the TSCH aided by blacklisting technique. Complete design and implementation specifics are provided; and the results of experiments are analysed to show its advantages over existing TSCH. The main finding of this work is that A-TSCH can significantly improve the reliability of channel hopping scheme and thus provide better protection from interference for wireless sensor networks.

The cloudUPDRS app is a Class I medical device, namely an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK for the clinical assessment of the motor symptoms of Parkinson's Disease. The app follows closely the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or in the community; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. This paper discusses how the cloudUPDRS system addresses two key challenges towards meeting essential consistency and efficiency requirements, namely: (i) How to ensure high-quality data collection especially considering the unsupervised nature of the test, in particular, how to achieve firm user adherence to the prescribed movements; and (ii) How to reduce test duration from approximately 25 minutes typically required by an experienced patient, to below 4 minutes, a threshold identified as critical to obtain significant improvements in clinical compliance. To address the former, we combine a bespoke design of the user experience tailored so as to constrain context, with a deep learning approach used to identify failures to follow the movement protocol while at the same time limiting false positives to avoid unnecessary repetition. We address the latter by developing a machine learning approach to personalise assessments by selecting those elements of the UPDRS protocol that most closely match individual symptom profiles and thus offer the highest inferential power hence closely estimating the patent's overall UPRDS score. • Personalised tests reducing the time required to carry out an assessment to less than 4 minutes. These so-called quick tests are created using machine learning to select a subset of UPDRS observations that closely estimate the motor performance of a particular patient.