Parkinson\u27s Symptoms quantification using wearable sensors

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting more than one million people in the United States and seven million people worldwide. Motor symptoms such as tremor, slowness of movements, rigidity, postural instability, and gait impairment are commonly observed in PD patients. Currently, Parkinsonian symptoms are usually assessed in clinical settings, where a patient has to complete some predefined motor tasks. Then a physician assigns a score based on the United Parkinson’s Disease Rating Scale (UPDRS) after observing the motor task. However, this procedure suffers from inter subject variability. Also, patients tend to show fewer symptoms during clinical visit, which leads to false assumption of the disease severity. The objective of this study is to overcome this limitations by building a system using Inertial Measurement Unit (IMU) that can be used at clinics and in home to collect PD symptoms data and build algorithms that can quantify PD symptoms more effectively. Data was acquired from patients seen at movement disorders Clinic at Sanford Health in Fargo, ND. Subjects wore Physilog IMUs and performed tasks for tremor, bradykinesia and gait according to the protocol approved by Sanford IRB. The data was analyzed using modified algorithm that was initially developed using data from normal subjects emulating PD symptoms. For tremor measurement, the study showed that sensor signals collected from the index finger more accurately predict tremor severity compared to signals from a sensor placed on the wrist. For finger tapping, a task measuring bradykinesia, the algorithm could predict with more than 80% accuracy when a set of features were selected to train the prediction model. Regarding gait, three different analysis were done to find the effective parameters indicative of severity of PD. Gait speed measurement algorithm was first developed using treadmill as a reference. Then, it was shown that the features selected could predict PD gait with 85.5% accuracy

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies, and provide possible future directions. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature

Wearable inertial sensors for human movement analysis

Introduction: The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.Areas covered: Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice.Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine

Body sensor networks: smart monitoring solutions after reconstructive surgery

Advances in reconstructive surgery are providing treatment options in the face of major trauma and cancer. Body Sensor Networks (BSN) have the potential to offer smart solutions to a range of clinical challenges. The aim of this thesis was to review the current state of the art devices, then develop and apply bespoke technologies developed by the Hamlyn Centre BSN engineering team supported by the EPSRC ESPRIT programme to deliver post-operative monitoring options for patients undergoing reconstructive surgery. A wireless optical sensor was developed to provide a continuous monitoring solution for free tissue transplants (free flaps). By recording backscattered light from 2 different source wavelengths, we were able to estimate the oxygenation of the superficial microvasculature. In a custom-made upper limb pressure cuff model, forearm deoxygenation measured by our sensor and gold standard equipment showed strong correlations, with incremental reductions in response to increased cuff inflation durations. Such a device might allow early detection of flap failure, optimising the likelihood of flap salvage. An ear-worn activity recognition sensor was utilised to provide a platform capable of facilitating objective assessment of functional mobility. This work evolved from an initial feasibility study in a knee replacement cohort, to a larger clinical trial designed to establish a novel mobility score in patients recovering from open tibial fractures (OTF). The Hamlyn Mobility Score (HMS) assesses mobility over 3 activities of daily living: walking, stair climbing, and standing from a chair. Sensor-derived parameters including variation in both temporal and force aspects of gait were validated to measure differences in performance in line with fracture severity, which also matched questionnaire-based assessments. Monitoring the OTF cohort over 12 months with the HMS allowed functional recovery to be profiled in great detail. Further, a novel finding of continued improvements in walking quality after a plateau in walking quantity was demonstrated objectively. The methods described in this thesis provide an opportunity to revamp the recovery paradigm through continuous, objective patient monitoring along with self-directed, personalised rehabilitation strategies, which has the potential to improve both the quality and cost-effectiveness of reconstructive surgery services.Open Acces

Objective assessment of motor and gait parameters of patients with multiple sclerosis

Multiple Sclerosis (MS) is a chronic inflammatory disease of the central nervous system. It affects approximately 400.000 individuals in Europe and about 2.5 million worldwide. Clinical symptoms of MS are highly variable and depend on the localization of lesions in the brain and spinal cord. Patients with chronic progressive neurological diseases such as MS typically show a decrease of physical activity as compared with healthy individuals. Approximately 75 to 80 percent of patients with MS (PwMS) experience walking and physical activity impairment in early stages of the disease. Therefore, walking impairment is considered as a hallmark symptom as this may have a significant impact on different daily activities. Moreover, an indirect association between overall MS symptoms and physical activity was found. Several studies investigated the walking ability and physical activity under free-living conditions in PwMS, as this may provide significant information to predict the patient’s health status. Different methods have been used for this purpose, including subjective approaches like self-report, questionnaires or diary methods. Although these methods are inexpensive and can easily be employed preferably in large scale studies, they are prone to error due to memory failure and other kind of misreporting. For many years, laboratory analysis systems have been considered to be the “gold standard” for physical activity and walking ability assessment. Nevertheless, these methods require extensive technical support and are unable to assess unconstrained physical activities in free-living situations. Thus, there is increasing interest in ambulatory assessment methods that provide objective measures of physical activity and gait parameters. Therefore, this thesis takes a different approach and investigate the usage of an objective monitoring system to early detect the slightly changes in disease-related walking ability and gait abnormality using one accelerometer. Moreover, this work aims to classify the derived acceleration data regarding their response to a certain intervention and treatment. In doing so, first of all, different algorithms were developed to extract activity and gait parameters in time, frequency and time-frequency domain. Then a Home-based system was developed and provided to help doctors monitor the changes in the ambulatory physical activity of PwMS objectively. The developed system was applied in two different studies over long period of time (one year) to assess changes in physical activity and gait behavior of PwMS and to classify their response to medical treatment. The aim of the first study was to investigate the ability of the developed parameters to objectively capture the changes in motor and walking ability in PwMS. Moreover, the objective was to provide additional evidence from long-term design study that support the association between changes in physical activity and walking ability and disease progression over time. The aim of the second study was to investigate the effectiveness of the medication treatment using the developed gait parameters and the assessment system developed in this work. The result of the study was compared to those assessed in the clinic. Comprehensive analysis of gait features in frequency and time-frequency domain can provide complementary information to understand gait patterns. Therefore, in this study, the parameters peak frequency and energy concentration were integrated along with time-domain parameters, such as step counts and walking speed. In case of chronic diseases, such as MS, medical benefit is the main factor to accept new technology. Thus, the developed system should be advantageous for diagnosis and therapy of MS. Moreover, it is important for the physician to be able to get better overview of the medical data about the disease course and health condition of their patients. Therefore, many critical factors regarding medical, technical and user specific aspects were considered in this work while developing the ambulatory assessment system. To assess the acceptance of the system a questionnaire was designed with main focus on two factors; usefulness and ease-of-use. The questionnaire was based on the Technology Acceptance Model (TAM). As a result, the design, validation and clinical application of Home-based monitoring system and algorithmic methods developed in this thesis offer the opportunity to comprehensively and objectively assess the pattern of behavioral change in physical activity and walking ability using one sensor across prolonged periods of time. The derived information may assist in the process of clinical decision making in the context of neurological rehabilitation and intervention (evaluation of medication or physiotherapy effects) and thus help to eventually improve the patients’ quality of life. In this work the focus was on patients with multiple sclerosis, however the developed and evaluated system can be adapted to other chronic diseases with physical activity disorders and impairment of gait

Exploring the use of wearables in the management of mild traumatic brain injury

a) Why is the subject of your thesis important? Every year more than 1 million people attend Accident and Emergency with mild traumatic brain injuries (mTBI), many of which arise from Sports Related Concussion (SRC). Despite the high incidence of such injuries, there is still no gold standard method to monitor the wide variety of impairments (cognitive, visual, motor symptom) accompanying mTBI. Accordingly, there is concern about the long-term effects of mTBI if diagnosis is delayed or missed entirely. Current reliance on subjective techniques such as symptoms are non-specific and an unreliable indicator of recovery, making it difficult to know when it is safe for players to return to play (RTP). This highlights the need for testing and validating the accuracy and applicability of objective tools to aid diagnosis, monitoring, and RTP protocols for individuals exposed to mTBI and SRC. b) How have you undertaken the research? I have taken a systematic approach to this problem-based research, starting by understanding the clinical challenges of mTBI from SRC where amateur rugby union is used as an exemplar for investigation throughout the thesis. Both mTBI and SRC is an under-researched area confounded by insufficient medical staff available to recognise SRC and monitor players within low resource (community) based settings. This may place these individuals at an increased risk of having a delayed diagnosis or it being missed entirely. My hypothesis tests if the use of digital technologies may enable affordable mTBI management, ensuring continuity and objective personalised assessment to support traditional approaches. Accordingly, my thesis broadly comprises of a literature examination and preliminary validation and testing, progressing to an in-depth exploration involving larger datasets and concluding with recommendations for clinical practice. c) What are your main research findings? My multidisciplinary approach reveals that focusing on one impairment in mTBI is unlikely to reveal meaningful insight to mTBI/SRC and RTP. Instead, multimodal digital technologies could enable affordable management, ensuring consistency and continuity (e.g., between assessors) while offering objective personalised data to better support traditional approaches. My results provide insight and identify the usefulness of instrumented walking (gait) as a digital (bio) marker for mTBI management. Based on receiver operating characteristics (ROC) and area under the curve (AUC) analyisis free-living step velocity (i.e., walking speed) was the most sensitive (>0.72) at distinguishing healthy from acute SRC and may be useful for continuous monitoring and therefore informing SRC RTP. In a purely computing science context, my findings have uncovered challenges and opportunities for further refinement. For example, there is still room for more ‘no code’ solutions in gait and algorithm analysis. Few clinicians would have the technical skillsets for completing free-living gait analysis. Therefore, validated algorithms within a "drag and drop", click and collect approach is needed to meet the recommend approach of remote, free-living monitoring of habitual behaviours. That is an important next step for the translation of academic research grade devices for broader deployment in clinical practice. d) Why do your research findings matter This thesis generally supports the suggested use of digital technologies as an affordable and objective method to support traditional approaches of assessment in mTBI/SRC. Passive and continuous monitoring solutions such as wearables are becoming ubiquitous in daily life. Moreover, the use of instrumented (lab) and free-living gait may fit that context with evidence of its use as a diagnostic tool. More work is needed to strengthen that claim as well as further investigate its use as a responsive tool. Identifying useful digital biomarkers in pathological cohorts such as mTBI may improve the detection of injuries and better inform safe (personalised) RTP guidelines. Identifying critical stages of recovery more accurately will also reduce the likelihood of premature return to play before full recovery, which is a necessary threshold in offering personalised care and rehabilitation. That is an important next step for the translation of academic research grade devices for broader deployment in clinical practice