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1.
Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi ; 34(11): 1369-1375, 2020 Nov 15.
Artigo em Chinês | MEDLINE | ID: mdl-33191692

RESUMO

Objective: To explore the gait trajectory characteristics and effectiveness after unicompartmental knee arthroplasty (UKA). Methods: Thirty patients (30 knees) with anterior medial compartment osteoarthritis who were treated with UKA between January 2017 and December 2018 were selected as subjects (UKA group). According to age, gender, and side, 30 patients (30 knees) with knee osteoarthritis treated with total knee arthroplasty (TKA) were selected as control (TKA group). In addition to the range of motion (ROM) before operation showing significant difference between the two groups ( t=4.25, P=0.00), there was no significant difference in gender, age, disease duration, sides, body mass index, and preoperative hip-knee-ankle angle (HKA), Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score between the two groups ( P>0.05). The incision length, drainage volume within 24 hours after operation, and the changes of hemoglobin and albumin were recorded. The WOMAC score, ROM, and HKA before and after operation were compared between the two groups. At 1 year after operation, the gait trajectory characteristics of two groups were analyzed by Vicon three-dimensional gait capture system, and the absolute symmetry index (ASI) of the lower limbs of the two groups was calculated. Results: The incisions of the two groups healed by first intention, with no complications. The incision length, drainage volume within 24 hours, and the changes of hemoglobin and albumin after operation in the UKA group were significantly smaller than those in the control group ( P<0.05). All patients were followed up completely, the follow-up time ranged from 13 to 20 months of UKA group (mean, 18 months) and 16 to 24 months of control group (mean, 20 months). The imaging review showed that the lower limb alignment of the two groups were restored to a neutral position, and the position of prosthesis was good. At 1 year after operation, the WOMAC score, HKA, and ROM of two groups were significantly improved when compared with those before operation ( P<0.05); the postoperative WOMAC score and ROM of the UKA group were significantly better than those of the control group ( P<0.05), and there was no significant difference in HKA between the two groups ( t=1.54, P=0.13). Gait analysis at 1 year after operation showed that the walking speed, stride length, knee extension at mid-stance, and flexion at swing in the UKA group were significantly better than those in the TKA group ( P<0.05); there was no significant difference in cadence, knee flexion at initial contact, and knee flexion at loading response between the two groups ( P>0.05). The ASI of bilateral knee flexion in the UKA group was significantly greater than that in the TKA group during the initial contact and loading response period ( P<0.05). Conclusion: Compared with TKA, UKA has the advantages of small incision, less blood loss, and quicker functional recovery. The early gait after UKA is mainly manifested as the increase in walking speed, stride length, knee flexion at swing, and extension at mid-stance phase. From the analysis of gait symmetry, during the initial contact and loading response phase, the operation side after UKA undertakes more shock absorption and joint stabilization functions than the contralateral side.


Assuntos
Artroplastia do Joelho , Prótese do Joelho , Osteoartrite do Joelho , Marcha , Humanos , Articulação do Joelho/cirurgia , Osteoartrite do Joelho/cirurgia , Amplitude de Movimento Articular , Resultado do Tratamento
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4109-4113, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018902

RESUMO

Human-machine interface with muscle signals serves as an important role in the field of wearable robotics. To compensate for the limitations of the existing surface Electromyography (sEMG) based technologies, we previously proposed a noncontact capacitive sensing approach that could record the limb shape changes. The sensing approach frees the human skin from contacting to the metal electrodes, thus enabling the measurement of muscle signals by dressing the sensing front-ends outside of the clothes. We validated the capacitive sensing in human motion intent recognition tasks with the wearable robots and produced comparable results to existing studies. However, the biological significance of the capacitance signals is still unrevealed, which is an indispensable issue for robot intuitive control. In this study, we address the problems of identifying the relationships between the muscle morphological parameters and the capacitance signals. We constructed a measurement system that recorded the noncon-tact capacitive sensing signals and the muscle ultrasound (US) images simultaneously. With the designed device, five subjects were employed and the US images from the gastrocnemius muscle (GM) and the tibialis anterior (TA) muscle during level walking were sampled. We fitted the calculated muscle morphological parameters (the pinnation angles and the muscle fascicle length) and the capacitance signals of the same gait phases. The results demonstrated that at least one-channel capacitance signal strongly correlated to the muscle morphological parameters (R2 > 0.5, quadratic regression). The average R2s of the most correlated channels were up to 0.86 for pinnation angles and 0.83 for the muscle fascicle length changes. The interesting findings in this preliminary study suggest the biological physical significance of the capacitance signals during human locomotion. Future efforts are worth being paid in this new research direction for more promising results.


Assuntos
Marcha , Caminhada , Capacitância Elétrica , Eletromiografia , Humanos , Locomoção
3.
Cochrane Database Syst Rev ; 10: CD006185, 2020 10 22.
Artigo em Inglês | MEDLINE | ID: mdl-33091160

RESUMO

BACKGROUND: Electromechanical- and robot-assisted gait-training devices are used in rehabilitation and might help to improve walking after stroke. This is an update of a Cochrane Review first published in 2007 and previously updated in 2017. OBJECTIVES: Primary • To determine whether electromechanical- and robot-assisted gait training versus normal care improves walking after stroke Secondary • To determine whether electromechanical- and robot-assisted gait training versus normal care after stroke improves walking velocity, walking capacity, acceptability, and death from all causes until the end of the intervention phase SEARCH METHODS: We searched the Cochrane Stroke Group Trials Register (last searched 6 January 2020); the Cochrane Central Register of Controlled Trials (CENTRAL; 2020 Issue 1), in the Cochrane Library; MEDLINE in Ovid (1950 to 6 January 2020); Embase (1980 to 6 January 2020); the Cumulative Index to Nursing and Allied Health Literature (CINAHL; 1982 to 20 November 2019); the Allied and Complementary Medicine Database (AMED; 1985 to 6 January 2020); Web of Science (1899 to 7 January 2020); SPORTDiscus (1949 to 6 January 2020); the Physiotherapy Evidence Database (PEDro; searched 7 January 2020); and the engineering databases COMPENDEX (1972 to 16 January 2020) and Inspec (1969 to 6 January 2020). We handsearched relevant conference proceedings, searched trials and research registers, checked reference lists, and contacted trial authors in an effort to identify further published, unpublished, and ongoing trials. SELECTION CRITERIA: We included all randomised controlled trials and randomised controlled cross-over trials in people over the age of 18 years diagnosed with stroke of any severity, at any stage, in any setting, evaluating electromechanical- and robot-assisted gait training versus normal care. DATA COLLECTION AND ANALYSIS: Two review authors independently selected trials for inclusion, assessed methodological quality and risk of bias, and extracted data. We assessed the quality of evidence using the GRADE approach. The primary outcome was the proportion of participants walking independently at follow-up. MAIN RESULTS: We included in this review update 62 trials involving 2440 participants. Electromechanical-assisted gait training in combination with physiotherapy increased the odds of participants becoming independent in walking (odds ratio (random effects) 2.01, 95% confidence interval (CI) 1.51 to 2.69; 38 studies, 1567 participants; P < 0.00001; I² = 0%; high-quality evidence) and increased mean walking velocity (mean difference (MD) 0.06 m/s, 95% CI 0.02 to 0.10; 42 studies, 1600 participants; P = 0.004; I² = 60%; low-quality evidence) but did not improve mean walking capacity (MD 10.9 metres walked in 6 minutes, 95% CI -5.7 to 27.4; 24 studies, 983 participants; P = 0.2; I² = 42%; moderate-quality evidence). Electromechanical-assisted gait training did not increase the risk of loss to the study during intervention nor the risk of death from all causes. Results must be interpreted with caution because (1) some trials investigated people who were independent in walking at the start of the study, (2) we found variation between trials with respect to devices used and duration and frequency of treatment, and (3) some trials included devices with functional electrical stimulation. Post hoc analysis showed that people who are non-ambulatory at the start of the intervention may benefit but ambulatory people may not benefit from this type of training. Post hoc analysis showed no differences between the types of devices used in studies regarding ability to walk but revealed differences between devices in terms of walking velocity and capacity. AUTHORS' CONCLUSIONS: People who receive electromechanical-assisted gait training in combination with physiotherapy after stroke are more likely to achieve independent walking than people who receive gait training without these devices. We concluded that eight patients need to be treated to prevent one dependency in walking. Specifically, people in the first three months after stroke and those who are not able to walk seem to benefit most from this type of intervention. The role of the type of device is still not clear. Further research should consist of large definitive pragmatic phase 3 trials undertaken to address specific questions about the most effective frequency and duration of electromechanical-assisted gait training, as well as how long any benefit may last. Future trials should consider time post stroke in their trial design.


Assuntos
Aparelhos Ortopédicos , Robótica/instrumentação , Reabilitação do Acidente Vascular Cerebral/métodos , Caminhada , Idoso , Viés , Causas de Morte , Terapia Combinada/instrumentação , Terapia Combinada/métodos , Intervalos de Confiança , Terapia por Estimulação Elétrica , Desenho de Equipamento , Terapia por Exercício/métodos , Marcha , Humanos , Pessoa de Meia-Idade , Razão de Chances , Ensaios Clínicos Controlados Aleatórios como Assunto , Reabilitação do Acidente Vascular Cerebral/instrumentação , Velocidade de Caminhada
4.
Arch. argent. pediatr ; 118(5): 343-347, oct 2020. tab
Artigo em Inglês, Espanhol | LILACS, BINACIS | ID: biblio-1122496

RESUMO

Se realizó un estudio transversal en escolares con corazón univentricular en estadio pos-bypass total de ventrículo derecho con el objetivo de determinar la capacidad funcional basal mediante el test de marcha en 6 minutos e identificar posibles factores determinantes. Participaron 30 pacientes con una mediana de edad de 12 años. Dieciocho pacientes fueron de sexo masculino. La mediana de distancia recorrida fue de 551,3 metros, un 84 % de la distancia teórica para población pediátrica sana. Las variables talla, presión arterial sistólica pretest y saturación arterial de oxígeno de reposo se asociaron significativamente con la distancia recorrida en el modelo de regresión lineal múltiple. No hubo asociación significativa en los metros caminados respecto de las variables sexo, estado nutricional, dignóstico cardiológico inicial, número de cirugías previas y edad al momento del bypass total de ventrículo derecho


A cross-sectional study was done in students with univentricular heart after undergoing total cavopulmonary connection (Fontan procedure) to establish their baseline functional capacity based on the six-minute walk test and identify potential determining factors. Thirty patients were included; their median age was 12 years old. Eighteen patients were males. The median distance walked was 551.3 meters, 84 % of the theoretical distance for a healthy pediatric population. Height, pre-test systolic blood pressure, and resting arterial oxygen saturation showed a significant association with the distance walked in the multiple linear regression model. No significant association was observed in the meters walked in terms of the following outcome measures: sex, nutritional status, baseline cardiological diagnosis, number of prior surgeries, and age at the time of Fontan procedure


Assuntos
Humanos , Masculino , Feminino , Criança , Adolescente , Coração Univentricular/diagnóstico , Estudantes , Estudos Transversais , Tolerância ao Exercício , Técnica de Fontan , Teste de Caminhada , Reabilitação Cardíaca , Marcha
5.
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5640-5643, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019256

RESUMO

Cerebellar Ataxia is a neurological disorder without an approved treatment. Patients will have impaired and uncoordinated motor functionality making them unable to complete their day-to-day activities. Ataxia clinics are established around the world to facilitate research and rehabilitate patients. However, the patients are generally evaluated by human - observation. Therefore, machine learning based data analysis is popular on motion captured via sensors. There are many neurological tests designed to analyse the motor impairments in different domains (such as upper limb, lower limb, gait, balance and speech). Clinicians follow scoring protocols to record the severity of patients for each domain test. This paper delivers a clinical assessment platform combining 12 neurological tests in 5 domains. It captures motion (from BioKin sensors), haptic and audio data (from the tablet or laptop screen). A data analysis system is hosted in a remote server which evaluates data to produce a severity score via different models built for each neurological test. The assessment platform clients and server communicate via a cloud buffer system. The scores input by the clinicians and predicted by the machine learning models are logged in the cloud database. This enables clinicians and doctors to view and compare the history of patient diagnosis. The server system is structured for automated score model upgrades via prompted approval. Thus, the most viable scoring model could be accommodated for each test based on longitudinal studies.


Assuntos
Ataxia Cerebelar , Ataxia , Ataxia Cerebelar/diagnóstico , Marcha , Humanos , Fala , Extremidade Superior
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5798-5801, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019292

RESUMO

Early detection of dementia is becoming increasingly important as it plays a crucial role in handling the patients and offering better treatment. Many of the recent studies concluded a tight relationship between dementia and gait disorders. For this purpose, identification of gait abnormalities is key factor. Novel technologies provide many options such as wearable and non-wearable approaches for analysis of gait. As the occurrence of dementia is more prominent in elderly people, wearable technology is considered out of scope for this work. The gait data of several elderly people over 80 years is acquired over certain intervals during the scope of the project. The elderly people are classified into three study groups namely cognitively healthy individuals (CHI), subjectively cognitively impaired persons (SCI) and possible mildly cognitively impaired persons due to inconclusive test results (pMCI) based on their cognitive status. The gait data is acquired using Kinect sensor. The acquired data consists of both RGB image sequences and depth data of the test persons. 3D human pose estimation is performed on this gait data and gait analysis is done. The transformations in the gait cycles are observed and the health condition of the individual is analyzed. From the analysis, the patterns in the gait abnormalities are correlated with the above-mentioned classification and are used in the detection of dementia in advance. The obtained results look promising and further analysis of gait parameters is under progress.


Assuntos
Demência , Dispositivos Eletrônicos Vestíveis , Idoso , Demência/diagnóstico , Diagnóstico Precoce , Marcha , Análise da Marcha , Humanos
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5935-5938, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019325

RESUMO

Early detection of chronic diseases helps to minimize the disease impact on patient's health and reduce the economic burden. Continuous monitoring of such diseases helps in the evaluation of rehabilitation program effectiveness as well as in the detection of exacerbation. The use of everyday wearables i.e. chest band, smartwatch and smart band equipped with good quality sensor and light weight machine learning algorithm for the early detection of diseases is very promising and holds tremendous potential as they are widely used. In this study, we have investigated the use of acceleration, electrocardiogram, and respiration sensor data from a chest band for the evaluation of obstructive lung disease severity. Recursive feature elimination technique has been used to identity top 15 features from a set of 62 features including gait characteristics, respiration pattern and heart rate variability. A precision of 0.93, recall of 0.91 and F-1 score of 0.92 have been achieved with a support vector machine for the classification of severe patients from the non-severe patients in a data set of 60 patients. In addition, the selected features showed significant correlation with the percentage of predicted FEV1.Clinical Relevance- The study result indicates that wearable sensor data collected during natural walk can be used in the early evaluation of pulmonary patients thus enabling them to seek medical attention and avoid exacerbation. In addition, it may serve as a complementary tool for pulmonary patient evaluation during a 6-minute walk test.


Assuntos
Doença Pulmonar Obstrutiva Crônica , Dispositivos Eletrônicos Vestíveis , Marcha , Humanos , Doença Pulmonar Obstrutiva Crônica/diagnóstico , Teste de Caminhada , Caminhada
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4487-4490, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018991

RESUMO

Wearable sensors have been investigated for the purpose of gait analysis, namely gait event detection. Many types of algorithms have been developed specifically using inertial sensor data for detecting gait events. Though much attention has turned toward machine learning algorithms, most of these approaches suffer from large computational requirements and are not yet suitable for real-time applications such as in prostheses or for feedback control. Current rules-based algorithms for real-time use often require fusion of multiple sensor signals to achieve high accuracy, thus increasing complexity and decreasing usability of the instrument. We present our results of a novel, rules-based algorithm using a single accelerometer signal from the foot to reliably detect heel-strike and toe-off events. Using the derivative of the raw accelerometer signal and applying an optimizer and windowing approach, high performance was achieved with a sensitivity and specificity of 94.32% and 94.70% respectively, and a timing error of 6.52 ± 22.37 ms, including trials involving multiple speed transitions. This would enable development of a compact wearable system for robust gait analysis in real-world settings, providing key insights into gait quality with the capability for real-time system control.


Assuntos
Algoritmos , Marcha , Acelerometria , Fenômenos Biomecânicos ,
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4592-4595, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019016

RESUMO

Gait analysis has many potential applications in understanding the activity profiles of individuals in their daily lives, particularly when studying the progression of recovery following injury, or motor deterioration in pathological conditions. One of the many challenges of conducting such analyses in the home environment is the correct and automatic identification of bouts of gait activity. To address this, a novel method for determining bouts of gait from accelerometer data recorded from the shank is presented. This method is fully automated and includes an adaptive thresholding approach which avoids the necessity for identifying subject-specific thresholds. The algorithm was tested on data recorded from 15 healthy subjects during self-selected slow, normal and fast walking speeds ranging from 0.48 ± 0.19 to 1.38 ± 0.33m/s and a single subject with PD walking at their normal walking speed (1.41 ± 0.08m/s) using accelerometers on the shanks. Intra-Class Correlation (ICC) confirmed high levels of agreement between bout onset/offset times and durations estimated using the algorithm, experimentally recorded stopwatch times and manual annotation for the healthy subjects (r=0.975, p <; 0.001; r=0.984, p<; 0.001) and moderate agreement for the PD subject (r=0.663, p<; 0.001). Mean absolute errors between accelerometer-derived and manually-annotated times were calculated, and ranged from 0.91 ± 0.05 s to 1.17 ± 2.26 s for bout onset detection, 0.80 ± 0.23 s to 2.41 ± 3.77 s for offset detection and 1.27 ± 0.13 s to 3.67 ± 4.59 s for bout durations.


Assuntos
Marcha , Caminhada , Acelerometria , Algoritmos , Humanos , Velocidade de Caminhada
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4596-4599, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019017

RESUMO

Walking speed (WS) is recognized as an important dimension of functional health and a candidate endpoint for clinical trials. To be adopted as a powerful outcome measure in clinical assessment, WS should be estimated pervasively and accurately in the real-life context. Although current state of the art points to possible solutions, e.g., by using pairing of wearable sensors with dedicated algorithms, the accuracy and robustness of existing algorithms in challenging situations should be carefully considered. This study highlights the main methodological issues for WS estimation using single inertial sensor fixed on trunk (chest/low back) and data recorded in a sample of stroke patients with impaired mobility.


Assuntos
Marcha , Acidente Vascular Cerebral , Algoritmos , Humanos , Tronco , Velocidade de Caminhada
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4676-4679, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019037

RESUMO

In this work, a new custom wireless capacitive step sensor and a real-time algorithm are proposed to detect the start and end of the swing phase of the gait to trigger the stimulation in Functional Electrical Stimulator devices (FES) for Drop Foot. For this, an array of capacitive pressure sensors was designed to detect patterns of the gait swing phase through the Heel Center of Pressure (HCOP). The proposed system recognized all the events with an average error of 20.86±0.02[ms] for the heel strike (initial contact) and 27.60±0.03[ms] for the heel-off (final contact) compared with lower-back accelerometer, constituting a viable, robust and promising alternative as a step sensor for functional electrical stimulators.


Assuntos
Terapia por Estimulação Elétrica , Transtornos Neurológicos da Marcha , Neuropatias Fibulares , Marcha , Calcanhar , Humanos
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4737-4740, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019049

RESUMO

Spinal cord injury (SCI) limits life expectancy and causes a restriction of patient's daily activities. In the last years, robotics exoskeletons have appeared as a promising rehabilitation and assistance tool for patients with motor limitations, as people that have suffered a SCI. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs), as they can be used to foster patients' neuroplasticity. However, there are not many studies showing the use of BMIs to control exoskeletons with patients. In this work we show a case study where one SCI patient has used a BMI based on motor imagery (MI) in order to control a lower limb exoskeleton that assists their gait.


Assuntos
Interfaces Cérebro-Computador , Exoesqueleto Energizado , Traumatismos da Medula Espinal , Marcha , Humanos , Extremidade Inferior
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4835-4838, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019073

RESUMO

Human joint impedance is a fundamental property of the neuromuscular system and describes the mechanical behavior of a joint. The identification of the lower limbs' joints impedance during locomotion is a key element to improve the design and control of active prostheses, orthoses, and exoskeletons. Joint impedance changes during locomotion and can be described by a linear time-varying (LTV) model. Several system identification techniques have been developed to retrieve LTV joint impedance, but these methods often require joint impedance to be consistent over multiple gait cycles. Given the inherent variability of neuromuscular control actions, this requirement is not realistic for the identification of human data. Here we propose the kernel-based regression (KBR) method with a locally periodic kernel for the identification of LTV ankle joint impedance. The proposed method considers joint impedance to be periodic yet allows for variability over the gait cycles. The method is evaluated on a simulation of joint impedance during locomotion. The simulation lasts for 10 gait cycles of 1.4 s each and has an output SNR of 15 dB. Two conditions were simulated: one in which the profile of joint impedance is periodic, and one in which the amplitude and the shape of the profile slightly vary over the periods. A Monte Carlo analysis is performed and, for both conditions, the proposed method can reconstruct the noiseless simulation output signal and the profiles of the time-varying joint impedance parameters with high accuracy (mean VAF ~ 99.9% and mean normalized RMSE of the parameters 1.33-4.06%).The proposed KBR method with a locally periodic kernel allows for the identification of periodic time-varying joint impedance with cycle-to-cycle variability.


Assuntos
Articulação do Tornozelo , Tornozelo , Impedância Elétrica , Marcha , Humanos , Locomoção
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5410-5415, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019204

RESUMO

Freezing of Gait is the most disabling gait disturbance in Parkinson's disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson's disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Dispositivos Eletrônicos Vestíveis , Marcha , Humanos , Extremidade Inferior , Redes Neurais de Computação , Doença de Parkinson/diagnóstico , Análise de Ondaletas
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 244-247, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017974

RESUMO

Freezing of gait (FOG) is a sudden cessation of locomotion in advanced Parkinson's disease (PD). A FOG episode can lead to falls, decreased mobility, and decreased overall quality of life. Prediction of FOG episodes provides an opportunity for intervention and freeze prevention. A novel method of FOG prediction that uses foot plantar pressure data acquired during gait was developed and evaluated, with plantar pressure data treated as 2D images and classified using a convolutional neural network (CNN). Data from five people with PD and a history of FOG were collected during walking trials. FOG instances were identified and data preceding each freeze were labeled as Pre-FOG. Left and right foot FScan pressure frames were concatenated into a single 60x42 pressure array. Each frame was considered as an independent image and classified as Pre-FOG, FOG, or Non-FOG, using the CNN. From prediction models using different Pre-FOG durations, shorter Pre-FOG durations performed best, with Pre-FOG class sensitivity 94.3%, and specificity 95.1%. These results demonstrated that foot pressure distribution alone can be a good FOG predictor when treating each plantar pressure frame as a 2D image, and classifying the images using a CNN. Furthermore, the CNN eliminated the need for feature extraction and selection.Clinical Relevance- This research demonstrated that foot plantar pressure data can be used to predict freezing of gait occurrence, using a convolutional neural network deep learning technique. This had the added advantage of eliminating the need for feature extraction and selection.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Marcha , Transtornos Neurológicos da Marcha/etiologia , Humanos , Redes Neurais de Computação , Qualidade de Vida
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 784-788, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018103

RESUMO

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson's disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pretrained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.


Assuntos
Doença de Parkinson , Atividades Cotidianas , Algoritmos , Marcha , Humanos , Redes Neurais de Computação
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 789-792, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018104

RESUMO

The analysis of gait data is one approach to support clinicians with the diagnosis and therapy of diseases, for example Parkinson's disease (PD). Traditionally, gait data of standardized tests in the clinic is analyzed, ensuring a predefined setting. In recent years, long-term home-based gait analysis has been used to acquire a more representative picture of the patient's disease status. Data is recorded in a less artificial setting and therefore allows a more realistic perception of the disease progression. However, fully unsupervised gait data without additional context information impedes interpretation. As an intermediate solution, performance of gait tests at home was introduced. Integration of instrumented gait test requires annotations of those tests for their identification and further processing. To overcome these limitations, we developed an algorithm for automatic detection of standardized gait tests from continuous sensor data with the goal of making manual annotations obsolete. The method is based on dynamic time warping, which compares an input signal with a predefined template and quantifies similarity between both. Different templates were compared and an optimized template was created. The classification scored a F1-measure of 86.7% for evaluation on a data set acquired in a clinical setting. We believe that this approach can be transferred to home-monitoring systems and will facilitate a more efficient and automated gait analysis.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Algoritmos , Marcha , Análise da Marcha , Humanos , Doença de Parkinson/diagnóstico
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 798-802, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018106

RESUMO

BACKGROUND: Parkinson's disease (PD) is a chronic condition that can be diagnosed and monitored by evaluating changes in the gait and arm movement parameters. In the gait movement, each cycle consists of two phases: stance and swing. Using gait analysis techniques, it is possible to get spatiotemporal variables derived from both phases. OBJECTIVE: In this paper, we compared two techniques: wavelet and peak detection. Previously, the wavelet technique was assessed for the gait phases detection, and peak detection was evaluated for arm swing analysis. These methods were evaluated using a low-cost RGB-D camera as data input source. This comparison could provide a unified and integrated method to analyze gait and arm swing signals. METHODS: Twenty-five PD patients and 25 age-matched, healthy subjects were included. Mann-Whitney U test was used to compare the continuous variables between groups. Hamming distances and Spearman rank correlation were used to evaluate the agreement between the signals and the spatiotemporal variables obtained by both methods. RESULTS: PD group showed significant reductions in speed (wavelet p = 0.001, peak detection p <0.001) and significantly greater swing (wavelet p = 0.003, peak detection p =0.005) and stance times (wavelet p = 0.003, peak detection p =0.004). Hamming distances showed small differences between the signals obtained by both methods (16 to 18 signal points). A very strong correlation (Spearman rho > 0.8, p <0.05) was found between the spatiotemporal variables obtained by each signal processing technique. CONCLUSION: Wavelet and peak detection techniques showed a high agreement in the signal obtained from gait data. The spatiotemporal variables obtained by both methods showed significant differences between the walking patterns of PD patients and healthy subjects. The peak detection technique can be used for integral motion analysis, providing the identification of the phases in the gait cycle, and arm swing parameters.Clinical Relevance- this establishes that peaks and wavelet techniques are comparable and may use it interchangeably to process signals from the gait of Parkinson's disease patients to support diagnosis and follow up made by a clinical expert.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Marcha , Análise da Marcha , Humanos , Doença de Parkinson/diagnóstico , Caminhada
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 816-819, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018110

RESUMO

Human observer-based assessments of Cerebellar Ataxia (CA) are subjective and are often inadequate to track mild motor symptoms. This study examines the potential use of a comprehensive sensor-based approach for objective evaluation of CA in five domains (speech, upper limb, lower limb, gait and balance) through the instrumented versions of nine bedside neurological tests. A total of twenty-three participants diagnosed with CA to varying degrees and eleven healthy controls were recruited. Data was collected using wearable inertial sensors and Kinect camera. In our study, an optimal feature subset based on feature importance in the Random Forest classifier model demonstrated an impressive performance accuracy of 97% (F1 score = 95.2%) for CA-control discrimination. Our experimental findings also indicate that the Romberg test contributed most, followed by the peripheral tests, while the Gait test contributed least to the classification. Sensor-based approaches, therefore, have the potential to complement existing clinical assessment techniques, offering advantages in terms of consistency, objectivity and informed clinical decision-making.


Assuntos
Ataxia Cerebelar , Ataxia Cerebelar/diagnóstico , Marcha , Humanos , Reprodutibilidade dos Testes , Fala , Extremidade Superior
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