Time measurement characterization of stand-to-sit and sit-to-stand transitions by using a smartphone

The aim of this study is to analyze a common method to measure the acceleration of a daily activity pattern by using a smartphone. In this sense, a numerical approach is proposed to transform the relative acceleration signal, recorded by a triaxial accelerometer, into an acceleration referred to an inertial reference. The integration of this acceleration allows to determine the velocity and position with respect to an inertial reference. Two different kinematic parameters are suggested to characterize the profile of the velocity during the sit-to-stand and stand-to-sit transitions for Parkinson and control subjects. The results show that a dimensionless kinematic parameter, which is linked to the time of sit-to-stand and stand-to-sit transitions, has the potential to differentiate between Parkinson and control subjects.Peer ReviewedPreprin

Neurological Tremor: Sensors, Signal Processing and Emerging Applications

Neurological tremor is the most common movement disorder, affecting more than 4% of elderly people. Tremor is a non linear and non stationary phenomenon, which is increasingly recognized. The issue of selection of sensors is central in the characterization of tremor. This paper reviews the state-of-the-art instrumentation and methods of signal processing for tremor occurring in humans. We describe the advantages and disadvantages of the most commonly used sensors, as well as the emerging wearable sensors being developed to assess tremor instantaneously. We discuss the current limitations and the future applications such as the integration of tremor sensors in BCIs (brain-computer interfaces) and the need for sensor fusion approaches for wearable solutions

Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis

BACKGROUND: With the growing number of the aged population, the number of Parkinson's disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients' symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries. METHOD: This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients' feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress. RESULT: The highest accuracy in PD detection using offline data was [Formula: see text] from voice data and [Formula: see text] from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient's gender, we could improve the detection accuracy. This study's novelty is the application of ensemble averaging on the combined decisions generated from the analysis of voice and tremor data. The average accuracy of PD detection becomes [Formula: see text] when ensemble averaging was performed on majority-vote from kNN, SVM, and NB. CONCLUSION: The proposed system can detect PD using a cloud-based system for computation, data preserving, and regular monitoring of voice and tremor samples captured by smartphones. Thus, this system can be a solution for healthcare authorities to ensure the older population's accessibility to a better medical diagnosis system in the developing countries, especially in the pandemic situation like COVID-19, when in-person monitoring is minimal

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI) : Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.Peer reviewe

Exploration of digital biomarkers in chronic low back pain and Parkinson’s disease

Chronic pain and Parkinson’s disease are illnesses with personal disease progression, symptoms, and the experience of these. The ability to measure and monitor the symptoms by digitally and remotely is still limited. The aim was to study the usability and feasibility of real-world data from wearables, mobile devices, and patients in exploring digital biomarkers in these diseases. The key hypothesis was that this allows us to measure, analyse and detect clinically valid digital signals in movement, heart rate and skin conductance data. The laboratory grade data in chronic pain were collected in an open feasibility study by using a program and built-in sensors in virtual reality devices. The real-world data were collected with a randomized clinical study by clinical assessments, built-in sensors, and two wearables. The laboratory grade dataset in Parkinson’s disease was obtained from Michael J. Fox Foundation. It contained sensor data from three wearables with clinical assessments. The real-world data were collected with a clinical study by clinical assessments, a wearable, and a mobile application. With both diseases the laboratory grade data were first explored, before the real-world data were analyzed. The classification of chronic pain patients with the laboratory grade movement data was possible with a high accuracy. A novel real-world digital signal that correlates with clinical outcomes was found in chronic low back pain patients. A model that was able to detect different movement states was developed with laboratory grade Parkinson’s disease data. A detection of these states followed by the quantification of symptoms was found to be a potential method for the future. The usability of data collection methods in both diseases were found promising. In the future the analyses of movement data in these diseases could be further researched and validated as a movement based digital biomarkers to be used as a surrogate or additional endpoint. Combining the data science with the optimal usability enables the exploitation of digital biomarkers in clinical trials and treatment.Digitaalisten biomarkkereiden tunnistaminen kroonisessä alaselkäkivussa ja Parkinsonin taudissa Krooninen kipu ja Parkinsonin tauti ovat oireiden, oirekokemuksen sekä taudin kehittymisen osalta yksilöllisiä sairauksia. Kyky mitata ja seurata oireita etänä on vielä alkeellista. Väitöskirjassa tutkittiin kaupallisten mobiili- ja älylaitteiden hyödyntämistä digitaalisten biomarkkereiden löytämisessä näissä taudeissa. Pääolettamus oli, että kaupallisten älylaitteiden avulla kyetään tunnistamaan kliinisesti hyödyllisiä digitaalisia signaaleja. Kroonisen kivun laboratorio-tasoinen data kerättiin tätä varten kehitettyä ohjelmistoa sekä kaupallisia antureita käyttäen. Reaaliaikainen kipudata kerättiin erillisen hoito-ohjelmiston tehoa ja turvallisuutta mitanneessa kliinisessä tutkimuksessa sekä kliinisiä arviointeja että anturidataa hyödyntäen. Laboratorio-tasoinena datana Parkinsonin taudissa käytettiin Michael J. Fox Foundationin kolmella eri älylaitteella ja kliinisin arvioinnein kerättyä dataa. Reaaliaikainen data kerättiin käyttäen kliinisia arviointeja, älyranneketta ja mobiilisovellusta. Molempien indikaatioiden kohdalla laboratoriodatalle tehtyä eksploratiivista analyysia hyödynnettiin itse reaaliaikaisen datan analysoinnissa. Kipupotilaiden tunnistaminen laboratorio-tasoisesta liikedatasta oli mahdollista korkealla tarkkuudella. Reaaliaikaisesta liikedatasta löytyi uusi kliinisten arviointien kanssa korreloiva digitaalinen signaali. Parkinsonin taudin datasta kehitettiin uusi liiketyyppien tunnistamiseen tarkoitettu koneoppimis-malli. Sen hyödyntäminen liikedatan liiketyyppien tunnistamisessa ennen varsinaista oireiden mittausta on lupaava menetelmä. Käytettävyys molempien tautien reaaliaikaisissa mittausmenetelmissä havaittiin toimivaksi. Reaaliaikaiseen, kaupallisin laittein kerättävään liikedataan pohjautuvat digitaaliset biomarkkerit ovat lupaava kohde jatkotutkimukselle. Uusien analyysimenetelmien yhdistäminen optimaaliseen käytettävyyteen mahdollistaa tulevaisuudessa digitaalisten biomarkkereiden hyödyntämisen sekä kroonisten tautien kliinisessä tutkimuksessa että itse hoidossa

iHandU Simulator - Towards a medical education tool for wrist rigidity assessment

During Deep Brain Stimulation (DBS) surgery, electrodes are implanted in the patient's brain in order to alleviate motor symptoms of common disorders such as Essential Tremor (ET) and Parkinson's Disease (PD). Stimulation parameters and electrode position are adjusted during surgery, chosen in order to obtain the best improvement in the patient's symptoms. The most commonly assessed symptom is muscular rigidity, which is characterized by an increased resistance to movement marked by a permanently elevated muscle contraction in response to a passive stretch. The assessment typically results from a series of flexions and extensions of a chosen joint, and consists of a qualitative improvement given according to a subjective scale, which is susceptible to errors, and has a verified lack of consistency among different clinicians. Robotic simulation of this behavior is of interest not only as a way to develop an educational tool for a unified method of rigidity assessment, but also in the context of aiding the development of decision support systems such as the iHandU, a wearable device in development at INESC-TEC

Assessment of Hand Gestures Using Wearable Sensors and Fuzzy Logic

Hand dexterity and motor control are critical in our everyday lives because a significant portion of the daily motions we perform are with our hands and require some degree of repetition and skill. Therefore, development of technologies for hand and extremity rehabilitation is a significant area of research that will directly help patients recovering from hand debilities sustained from causes ranging from stroke and Parkinson’s disease to trauma and common injuries. Cyclic activity recognition and assessment is appropriate for hand and extremity rehabilitation because a majority of our essential motions are cyclic in their nature. For a patient on the road to regaining functional independence with daily skills, the improvement in cyclic motions constitutes an important and quantifiable rehabilitation goal. However, challenges exist with hand rehabilitation sensor technologies preventing acquisition of long-term, continuous, accurate and actionable motion data. These challenges include complicated and uncomfortable system assemblies, and a lack of integration with consumer electronics for easy readout. In our research, we have developed a glove based system where the inertial measurement unit (IMU) sensors are used synergistically with the flexible sensors to minimize the number of IMU sensors. The classification capability of our system is improved by utilizing a fuzzy logic data analysis algorithm. We tested a total of 25 different subjects using a glove-based apparatus to gather data on two-dimensional motions with one accelerometer and three-dimensional motions with one accelerometer and two flexible sensors. Our research provides an approach that has the potential to utilize both activity recognition and activity assessment using simple sensor systems to help patients recover and improve their overall quality of life

Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments