Wearable sensors and systems. From enabling technology to clinical applications

Role of body-worn movement monitor technology for balance and gait rehabilitation

This perspective article will discuss the potential role of body-worn movement monitors for balance and gait assessment and treatment in rehabilitation. Recent advances in inexpensive, wireless sensor technology and smart devices are resulting in an explosion of miniature, portable sensors that can quickly and accurately quantify body motion. Practical and useful movement monitoring systems are now becoming available. It is critical that therapists understand the potential advantages and limitations of such emerging technology. One important advantage of obtaining objective measures of balance and gait from body-worn sensors is impairment-level metrics characterizing how and why functional performance of balance and gait activities are impaired. Therapy can then be focused on the specific physiological reasons for difficulty in walking or balancing during specific tasks. A second advantage of using technology to measure balance and gait behavior is the increased sensitivity of the balance and gait measures to document mild disability and change with rehabilitation. A third advantage of measuring movement, such as postural sway and gait characteristics, with body-worn sensors is the opportunity for immediate biofeedback provided to patients that can focus attention and enhance performance. In the future, body-worn sensors may allow therapists to perform telerehabilitation to monitor compliance with home exercise programs and the quality of their natural mobility in the community. Therapists need technological systems that are quick to use and provide actionable information and useful reports for their patients and referring physicians. Therapists should look for systems that provide measures that have been validated with respect to gold standard accuracy and to clinically relevant outcomes such as fall risk and severity of disability.

Piezo-phototronic Boolean logic and computation using photon and strain dual-gated nanowire transistors

Using polarization charges created at the metal-cadmium sulfide interface under strain to gate/modulate electrical transport and optoelectronic processes of charge carriers, the piezo-phototronic effect is applied to process mechanical and optical stimuli into electronic controlling signals. The cascade nanowire networks are demonstrated for achieving logic gates, binary computations, and gated D latches to store information carried by these stimuli.

Transparent conducting films of hierarchically nanostructured polyaniline networks on flexible substrates for high-performance gas sensors

Transparent chemical gas sensors are assembled from a transparent conducting film of hierarchically nanostructured polyaniline (PANI) networks fabricated on a flexible PET substrate, by coating silver nanowires (Ag NWs) followed by the in situ polymerization of aniline near the sacrificial Ag NW template. The sensor exhibits enhanced gas sensing performance at room temperature in both sensitivity and selectivity to NH3 compared to pure PANI film.

Non-invasive mouthguard biosensor for continuous salivary monitoring of metabolites

The present work describes the first example of a wearable salivary metabolite biosensor based on the integration of a printable enzymatic electrode on a mouthguard. The new mouthguard enzymatic biosensor, based on an immobilized lactate oxidase and a low potential detection of the peroxide product, exhibits high sensitivity, selectivity and stability using whole human saliva samples. Such non-invasive mouthguard metabolite biosensors could tender useful real-time information regarding a wearer's health, performance and stress level, and thus hold considerable promise for diverse biomedical and fitness applications.

Technical and clinical view on ambulatory assessment in Parkinson's disease

With the progress of technologies of recent years, methods have become available that use wearable sensors and ambulatory systems to measure aspects of--particular axial--motor function. As Parkinson's disease (PD) can be considered a model disorder for motor impairment, a significant number of studies have already been performed with these patients using such techniques. In general, motion sensors such as accelerometers and gyroscopes are used, in combination with lightweight electronics that do not interfere with normal human motion. A fundamental advantage in comparison with usual clinical assessment is that these sensors allow a more quantitative, objective, and reliable evaluation of symptoms; they have also significant advantages compared to in-lab technologies (e.g., optoelectronic motion capture) as they allow long-term monitoring under real-life conditions. In addition, based on recent findings particularly from studies using functional imaging, we learned that non-motor symptoms, specifically cognitive aspects, may be at least indirectly assessable. It is hypothesized that ambulatory quantitative assessment strategies will allow users, clinicians, and scientists in the future to gain more quantitative, unobtrusive, and everyday relevant data out of their clinical evaluation and can also be designed as pervasive (everywhere) and intensive (anytime) tools for ambulatory assessment and even rehabilitation of motor and (partly) non-motor symptoms in PD.

Stretchable, wireless sensors and functional substrates for epidermal characterization of sweat

This paper introduces materials and architectures for ultrathin, stretchable wireless sensors that mount on functional elastomeric substrates for epidermal analysis of biofluids. Measurement of the volume and chemical properties of sweat via dielectric detection and colorimetry demonstrates some capabilities. Here, inductively coupled sensors consisting of LC resonators with capacitive electrodes show systematic responses to sweat collected in microporous substrates. Interrogation occurs through external coils placed in physical proximity to the devices. The substrates allow spontaneous sweat collection through capillary forces, without the need for complex microfluidic handling systems. Furthermore, colorimetric measurement modes are possible in the same system by introducing indicator compounds into the depths of the substrates, for sensing specific components (OH(-) , H(+) , Cu(+) , and Fe(2+) ) in the sweat. The complete devices offer Young's moduli that are similar to skin, thus allowing highly effective and reliable skin integration without external fixtures. Experimental results demonstrate volumetric measurement of sweat with an accuracy of 0.06 μL/mm(2) with good stability and low drift. Colorimetric responses to pH and concentrations of various ions provide capabilities relevant to analysis of sweat. Similar materials and device designs can be used in monitoring other body fluids.

Telemonitoring with respect to mood disorders and information and communication technologies: overview and presentation of the PSYCHE project

This paper reviews what we know about prediction in relation to mood disorders from the perspective of clinical, biological, and physiological markers. It then also presents how information and communication technologies have developed in the field of mood disorders, from the first steps, for example, the transition from paper and pencil to more sophisticated methods, to the development of ecological momentary assessment methods and, more recently, wearable systems. These recent developments have paved the way for the use of integrative approaches capable of assessing multiple variables. The PSYCHE project stands for Personalised monitoring SYstems for Care in mental HEalth.

Use of a Wireless Network of Accelerometers for Improved Measurement of Human Energy Expenditure

Single, hip-mounted accelerometers can provide accurate measurements of energy expenditure (EE) in some settings, but are unable to accurately estimate the energy cost of many non-ambulatory activities. A multi-sensor network may be able to overcome the limitations of a single accelerometer. Thus, the purpose of our study was to compare the abilities of a wireless network of accelerometers and a hip-mounted accelerometer for the prediction of EE. Thirty adult participants engaged in 14 different sedentary, ambulatory, lifestyle and exercise activities for five minutes each while wearing a portable metabolic analyzer, a hip-mounted accelerometer (AG) and a wireless network of three accelerometers (WN) worn on the right wrist, thigh and ankle. Artificial neural networks (ANNs) were created separately for the AG and WN for the EE prediction. Pearson correlations (r) and the root mean square error (RMSE) were calculated to compare criterion-measured EE to predicted EE from the ANNs. Overall, correlations were higher (r = 0.95 vs. r = 0.88, p < 0.0001) and RMSE was lower (1.34 vs. 1.97 metabolic equivalents (METs), p < 0.0001) for the WN than the AG. In conclusion, the WN outperformed the AG for measuring EE, providing evidence that the WN can provide highly accurate estimates of EE in adults participating in a wide range of activities.

Compression in wearable sensor nodes: impacts of node topology

Wearable sensor nodes monitoring the human body must operate autonomously for very long periods of time. Online and low-power data compression embedded within the sensor node is therefore essential to minimize data storage/transmission overheads. This paper presents a low-power MSP430 compressive sensing implementation for providing such compression, focusing particularly on the impact of the sensor node architecture on the compression performance. Compression power performance is compared for four different sensor nodes incorporating different strategies for wireless transmission/on-sensor-node local storage of data. The results demonstrate that the compressive sensing used must be designed differently depending on the underlying node topology, and that the compression strategy should not be guided only by signal processing considerations. We also provide a practical overview of state-of-the-art sensor node topologies. Wireless transmission of data is often preferred as it offers increased flexibility during use, but in general at the cost of increased power consumption. We demonstrate that wireless sensor nodes can highly benefit from the use of compressive sensing and now can achieve power consumptions comparable to, or better than, the use of local memory.

Feasibility study of a wearable system based on a wireless body area network for gait assessment in Parkinson's disease patients

Parkinson's disease (PD) alters the motor performance of affected individuals. The dopaminergic denervation of the striatum, due to substantia nigra neuronal loss, compromises the speed, the automatism and smoothness of movements of PD patients. The development of a reliable tool for long-term monitoring of PD symptoms would allow the accurate assessment of the clinical status during the different PD stages and the evaluation of motor complications. Furthermore, it would be very useful both for routine clinical care as well as for testing novel therapies. Within this context we have validated the feasibility of using a Body Network Area (BAN) of wireless accelerometers to perform continuous at home gait monitoring of PD patients. The analysis addresses the assessment of the system performance working in real environments.

Sensor network infrastructure for a home care monitoring system

This paper presents the sensor network infrastructure for a home care system that allows long-term monitoring of physiological data and everyday activities. The aim of the proposed system is to allow the elderly to live longer in their home without compromising safety and ensuring the detection of health problems. The system offers the possibility of a virtual visit via a teleoperated robot. During the visit, physiological data and activities occurring during a period of time can be discussed. These data are collected from physiological sensors (e.g., temperature, blood pressure, glucose) and environmental sensors (e.g., motion, bed/chair occupancy, electrical usage). The system can also give alarms if sudden problems occur, like a fall, and warnings based on more long-term trends, such as the deterioration of health being detected. It has been implemented and tested in a test environment and has been deployed in six real homes for a year-long evaluation. The key contribution of the paper is the presentation of an implemented system for ambient assisted living (AAL) tested in a real environment, combining the acquisition of sensor data, a flexible and adaptable middleware compliant with the OSGistandard and a context recognition application. The system has been developed in a European project called GiraffPlus.

An ambulatory method of identifying anterior cruciate ligament reconstructed gait patterns

The use of inertial sensors to characterize pathological gait has traditionally been based on the calculation of temporal and spatial gait variables from inertial sensor data. This approach has proved successful in the identification of gait deviations in populations where substantial differences from normal gait patterns exist; such as in Parkinsonian gait. However, it is not currently clear if this approach could identify more subtle gait deviations, such as those associated with musculoskeletal injury. This study investigates whether additional analysis of inertial sensor data, based on quantification of gyroscope features of interest, would provide further discriminant capability in this regard. The tested cohort consisted of a group of anterior cruciate ligament reconstructed (ACL-R) females and a group of non-injured female controls, each performed ten walking trials. Gait performance was measured simultaneously using inertial sensors and an optoelectronic marker based system. The ACL-R group displayed kinematic and kinetic deviations from the control group, but no temporal or spatial deviations. This study demonstrates that quantification of gyroscope features can successfully identify changes associated with ACL-R gait, which was not possible using spatial or temporal variables. This finding may also have a role in other clinical applications where small gait deviations exist.

A Survey on Ambient Intelligence in Health Care

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

Flexible sensors based on nanoparticles

Flexible sensors can be envisioned as promising components for smart sensing applications, including consumer electronics, robotics, prosthetics, health care, safety equipment, environmental monitoring, homeland security and space flight. The current review presents a concise, although admittedly nonexhaustive, didactic review of some of the main concepts and approaches related to the use of nanoparticles (NPs) in flexible sensors. The review attempts to pull together different views and terminologies used in the NP-based sensors, mainly those established via electrical transduction approaches, including, but, not confined to: (i) strain-gauges, (ii) flexible multiparametric sensors, and (iii) sensors that are unaffected by mechanical deformation. For each category, the review presents and discusses the common fabrication approaches and state-of-the-art results. The advantages, weak points, and possible routes for future research, highlighting the challenges for NP-based flexible sensors, are presented and discussed as well.

Usability and Interoperability in Wireless Sensor Networks for Patient Telemonitoring in Chronic Disease Management

This paper addresses two key technological barriers to the wider adoption of patient telemonitoring systems for chronic disease management, namely, usability and sensor device interoperability. As a great percentage of chronic patients are elderly patients as well, usability of the system has to be adapted to their needs. This paper identifies (from previous research) a set of design criteria to address these challenges, and describes the resulting system based on a wireless sensor network, and including a node as a custom-made interface that follows usability design criteria stated. This system has been tested with 22 users (mean age 65) and evaluated with a validated usability questionnaire. Results are good and improve those of other systems based on TV or smartphone. Our results suggest that user interfaces alternative to TVs and smartphones could play an important role on the usability of sensor networks for patient monitoring. Regarding interoperability, only very recently a standard has been published (2010, the ISO IEEE 11073 Personal health devices) that can support the needs of limited computational power environments typical of patient monitoring sensor networks.

Embedded fall and activity monitoring for a wearable ambient assisted living solution for older adults

With the rapidly increasing over 60 and over 80 age groups in society, greater emphasis will be put on technology to detect emergency situations, such as falls, in order to promote independent living. This paper describes the development and deployment of fall-detection, activity classification and energy expenditure algorithms, deployed in a tele-monitoring system. These algorithms were successfully tested in an end-user trial involving 9 elderly volunteers using the system for 28 days.

Comparison of median frequency between traditional and functional sensor placements during activity monitoring

Long-term monitoring is of great clinical relevance. Accelerometers are often used to provide information about activities of daily living. The median frequency (f_m) of acceleration has recently been suggested as a powerful parameter for activity recognition. However, compliance issues arise when people need to integrate activity recognition sensors into their daily lives. More functional placements should provide higher levels of conformity, but may also affect the quality and generalizability of the signals. How f_m changes as a result of a more functional sensor placement remains unclear. This study investigates the agreement in f_m for a sensor placed on the back with one in the pocket across a range of daily activities. The translational and gravitational accelerations are also computed to determine if the accelerometer should be fused with additional sensors to improve agreement. Twelve subjects were tested over four tasks and only the "vertical" x-axis showed a moderate agreement (Intraclass Correlation Coefficient of 0.54) after correction for outliers. Generalizability across traditional and functional sensor locations might therefore be limited. Differentiation of the signal into a translational and gravitational component decreased the level of agreement further, suggesting that combined information streams are more robust to changing locations then singular data streams. Integrating multiple sensor modalities to obtain specific components is unlikely to improve agreement across sensor locations. More research is needed to explore measurement signals of more user friendly sensor configurations that will lead to a greater clinical acceptance of body worn sensor systems.

Evaluation of Chewing and Swallowing Sensors for Monitoring Ingestive Behavior

Monitoring Ingestive Behavior (MIB) of individuals is of special importance to identify and treat eating patterns associated with obesity and eating disorders. Current methods for MIB require subjects reporting every meal consumed, which is burdensome and tend to increase the reporting bias over time. This study presents an evaluation of the burden imposed by two wearable sensors for MIB during unrestricted food intake: a strain sensor to detect chewing events and a throat microphone to detect swallowing sounds. A total of 30 healthy subjects with various levels of adiposity participated in experiments involving the consumption of four meals in four different visits. A questionnaire was handled to subjects at the end of the last visit to evaluate the sensors burden in terms of the comfort levels experienced. Results showed that sensors presented high comfort levels as subjects indicated that the way they ate their meal was not considerably affected by the presence of the sensors. A statistical analysis showed that chewing sensor presented significantly higher comfort levels than the swallowing sensor. The outcomes of this study confirmed the suitability of the chewing and swallowing sensors for MIB and highlighted important aspects of comfort that should be addressed to obtain acceptable and less burdensome wearable sensors for MIB.

Wearable and implantable sensors: the patient's perspective

There has been a rising interest in wearable and implantable biomedical sensors over the last decade. However, many technologies have not been integrated into clinical care, due to a limited understanding of user-centered design issues. Little information is available about these issues and there is a need to adopt more rigorous evidence standards for design features to allow important medical sensors to progress quicker into clinical care. Current trends in patient preferences need to be incorporated at an early stage into the design process of prospective clinical sensors. The first comprehensive patient data set, discussing mobile biomedical sensor technology, is presented in this paper. The study population mainly consisted of individuals suffering from arthritis. It was found that sensor systems needed to be small, discreet, unobtrusive and preferably incorporated into everyday objects. The upper extremity was seen as the favored position on the body for placement, while invasive placement yielded high levels of acceptance. Under these conditions most users were willing to wear the body-worn sensor for more than 20 h a day. This study is a first step to generate research based user-orientated design criteria’s for biomedical sensors.

Development of a platform to combine sensor networks and home robots to improve fall detection in the home environment

Over the last decade, significant progress has been made in the development of wearable sensor systems for continuous health monitoring in the home and community settings. One of the main areas of application for these wearable sensor systems is in detecting emergency events such as falls. Wearable sensors like accelerometers are increasingly being used to monitor daily activities of individuals at a risk of falls, detect emergency events and send alerts to caregivers. However, such systems tend to have a high rate of false alarms, which leads to low compliance levels. Home robots can enable caregivers with the ability to quickly make an assessment and intervene if an emergency event is detected. This can provide an additional layer for detecting false positives, which can lead to improve compliance. In this paper, we present preliminary work on the development of a fall detection system based on a combination sensor networks and home robots. The sensor network architecture comprises of body worn sensors and ambient sensors distributed in the environment. We present the software architecture and conceptual design home robotic platform. We also perform preliminary characterization of the sensor network in terms of latencies and battery lifetime.

Electroactive polymer patches for wearable haptic interfaces

Fully wearable and unobtrusive sensing will enable the possibility of monitoring people anywhere and anytime, for healthcare, well-being, protection and safety. Many research groups have exploited textiles as the ideal platform for pervasive monitoring. This paper reports advances in electroactive polymer technology oriented to mechanical sensing and actuation within textile interfaces. The preliminary development of a textile-based glove in which electroactive polymers act as force/position sensors and haptic feedback actuators is presented.

Optimum gravity vector and vertical acceleration estimation using a tri-axial accelerometer for falls and normal activities

This study aims to determine an optimum estimate for the gravitational vector and vertical acceleration profiles using a body-worn tri-axial accelerometer during falls and normal activities of daily living (ADL), validated using a camera based motion analysis system. Five young healthy subjects performed a number of simulated falls and normal ADL while trunk kinematics were measured by both an optical motion analysis system and a tri-axial accelerometer. Through low-pass filtering of the trunk tri-axial accelerometer signal between 1 Hz and 2.7 Hz using a 1(st) order or higher, Butterworth IIR filter, accurate gravity vector profile can be obtained using the method described here.

RESULTS

A high mean correlation (≥ 0.83: Coefficient of Multiple Correlations) and low mean percentage error (≤ 2.06 m/s(2)) were found between the vertical acceleration profile generated from the tri-axial accelerometer based sensor to those from the optical motion capture system. This proposed system enables optimum gravity vector and vertical acceleration profiles to be measured from the trunk during falls and normal ADL.

Smart home technology for telemedicine and emergency management

With the ageing population, mobility is an important issue and it deters the elderlies to visit health clinics on a regular basis. Individuals with disabilities also face the same obstacles for their out-of-home medical visits. In addition, people living in remote areas often do not get the needed health care attention unless they are willing to spend the time, effort and cost to travel. Advances in information and telecommunication technologies have made telemedicine possible. Using the latest sensor technologies, a person's vital data can be collected in a smart home environment. The bio-information can then be transferred wirelessly or via the Internet to medical databases and the healthcare professionals. Using the appropriate sensing apparatus at a smart home setting, patients, elderlies and people with disabilities can have their health signals and information examined on a real-time and archival basis. Recovery process can be charted on a regular basis. Remote emergency alerts can be intercepted and responded quickly. Health deterioration can be monitored closely enabling corrective actions. Medical practitioners can therefore provide the necessary health-related services to more people. This paper surveys and compiles the state-of-the-art smart home technologies and telemedicine systems.

A review of wearable sensors and systems with application in rehabilitation

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

Distilling clinically interpretable information from data collected on next-generation wearable sensors

Medical electronic systems are generating ever larger data sets from a variety of sensors and devices. Such systems are also being packaged in wearable designs for easy and broad use. The large volume of data and the constraints of low-power, extended-duration, and wireless monitoring impose the need for on-chip processing to distill clinically relevant information from the raw data. The higher-level information, rather than the raw data, is what needs to be transmitted. We present one example of information processing for continuous, high-sampling-rate data collected from wearable and portable devices. A wearable cardiac and motion monitor designed by colleagues at MIT simultaneously records electrocardiogram (ECG) and 3-axis acceleration to onboard memory, in an ambulatory setting. The acceleration data is used to generate a continuous estimate of physical activity. Additionally, we use a Portapres continuous blood pressure monitor to concurrently record the arterial blood pressure (ABP) waveform. To help reduce noise, which is an increased challenge in ambulatory monitoring, we use both the ECG and ABP waveforms to generate a robust measure of heart rate from noisy data. We also generate an overall signal abnormality index to aid in the interpretation of the results. Two important cardiovascular quantities, namely cardiac output (CO) and total peripheral resistance (TPR), are then derived from this data over a sequence of physical activities. CO and TPR can be estimated (to within a scale factor) from heart rate, pulse pressure and mean arterial blood pressure, which in turn are directly obtained from the ECG and ABP signals. Data was collected on 10 healthy subjects. The derived quantities vary in a manner that is consistent with known physiology. Further work remains to correlate these values with the cardiac health state.

Body Area Networks for ubiquitous healthcare applications: opportunities and challenges

Body Area Networks integrated into mHealth systems are becoming a mature technology with unprecedented opportunities for personalized health monitoring and management. Potential applications include early detection of abnormal conditions, supervised rehabilitation, and wellness management. Such integrated mHealth systems can provide patients with increased confidence and a better quality of life, and promote healthy behavior and health awareness. Automatic integration of collected information and user's inputs into research databases can provide medical community with opportunity to search for personalized trends and group patterns, allowing insights into disease evolution, the rehabilitation process, and the effects of drug therapy. A new generation of personalized monitoring systems will allow users to customize their systems and user interfaces and to interact with their social networks. With emergence of first commercial body area network systems, a number of system design issues are still to be resolved, such as seamless integration of information and ad-hoc interaction with ambient sensors and other networks, to enable their wider acceptance. In this paper we present state of technology, discuss promising new trends, opportunities and challenges of body area networks for ubiquitous health monitoring applications.

A system for activity recognition using multi-sensor fusion

This paper proposes a system for activity recognition using multi-sensor fusion. In this system, four sensors are attached to the waist, chest, thigh, and side of the body. In the study we present two solutions for factors that affect the activity recognition accuracy: the calibration drift and the sensor orientation changing. The datasets used to evaluate this system were collected from 8 subjects who were asked to perform 8 scripted normal activities of daily living (ADL), three times each. The Naïve Bayes classifier using multi-sensor fusion is adopted and achieves 70.88%-97.66% recognition accuracies for 1-4 sensors.

A web-based system for home monitoring of patients with Parkinson's disease using wearable sensors

This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson's disease (PD) using wearable sensors. MercuryLive contains three tiers: a resource-aware data collection engine that relies upon wearable sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when 800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor patients with PD to facilitate the titration of medications in the late stages of the disease.