![Observation, scaling, and measuring gait in PD. The first assessment of movement and related impairments in PD was the observation. Clinicians moved to a scaling to improve the comparability with e.g. the Hoehn and Yahr scale. Nowadays, body worn sensors attached to the shoe enable measurement of prototypical PD gait features. The figure is reused from [Schl 17] with the publisher's permission.](https://www.researchgate.net/publication/322751202/figure/fig1/AS:631615810191382@1527600352586/Observation-scaling-and-measuring-gait-in-PD-The-first-assessment-of-movement-and_Q320.jpg)
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Development and Validation of a Mobile Gait Analysis System Providing Clinically Relevant Target Parameters in Parkinson's Disease
Abstract and Figures
In 1817 James Parkinson described in his Essay on the Shaking Palsy that impaired gait is one of the most important symptoms in Parkinson’s disease (PD): "Walking becomes a task which cannot be performed without considerable attention. The legs are not raised to that height, or with that promptitude which the will directs,...". Gait disorders still play an important role in PD since the diagnosis, the staging of the disease level, and also the assessment of therapeutic outcomes are based mainly on the rating of movement impairments. The state-of-the-art scores and scales to rate PD are based solely on subjective assessment of movement disorder specialists. A more objective rating of disease related impairments is therefore an urgent need to support clinical decision making. In this thesis the development of a mobile and unobtrusive system to rate gait impairments with wearable sensors is described. An inertial measurement system was used to provide standard gait parameters and a scoring that rates PD related gait impairments. This system supplies physicians and therapists of movement disorders with objective and complementary data and consequently improves clinical decision making.
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... This is of course prone to information loss and requires the set of stride parameters to be sufficiently descriptive. Within this work, the spectrum of available stride parameters in the prototypical implementation [Bar17] is complemented with parameters drawn from literature as well as novel surrogate markers for ground reaction forces. The latter specifically support characterization of posturally impaired Parkinson's Disease (PD) patients. ...
... Barth [Bar17] presents a mobile gait analysis system based on two off-the-shelf IMUs located below the ankle and attached to a sport shoe. Early work evaluates proof of concept in terms of classification between healthy controls and PD patients based on IMU data captured during standardized gait tests [ Bar+11;Klu+13]. ...
... Nevertheless, generic features are very useful in classification and regression pipelines that produce clinically valuable information as an output. Barth [Bar17] gives a good overview on generic stride features in chapter 7.4 and presents several use-cases in the following chapters. Regarding biomechanically interpretable features, a distinction is commonly made between temporal and spatial parameters. ...
The aim of this thesis is to move mobile gait analysis systems based on inertial sensing closer towards clinical grade wearable devices. Such devices are envisioned to be used in everyday clinical practice for objective gait assessment under supervised conditions as well as for remote monitoring of gait in real-life environments. Such applications, however, require clinical grade of the wearable device established through clearance by the authorities and this process needs to be based on scientific research. The present thesis moves towards this aim in three main areas: Benchmarking methodological choices in foot trajectory reconstruction, extending the stride parameterization with kinetic features and reducing the assumption set current mobile gait analysis systems are built upon in order to widen the scope of gait disorders these systems can be used in.
... The research community has shown also a growing interest in the automatic gait analysis of PD. The assessment is performed commonly with inertial sensors attached to the body of the patients [8,9] and with force-sensitive sensors placed inside the shoes of the participants [10]. By using inertial sensors, it is possible to detect and to characterize specific movements and to monitor activities of daily living of PD patients [11]. ...
... The speech signals were recorded with a sampling frequency of 16 kHz and 16-bit resolution. The gait signals were captured with the eGaIT system, which consists of a 3D-accelerometer (range ±6g) and a 3D gyroscope (range ±500 • /s) attached to the external side (at the ankle level) of the shoes [9]. Data from both feet were captured with a sampling frequency of 100 Hz and 12-bit resolution. ...
... Previous studies [15][16][17][18] employed the DTW method to segment the signals, part of which matched well with a predefined reference pattern. Ghassemi et al. [15] employed two variations of this method in different experiments of PD patients walking for gait segmentation. ...
... They achieved a 97% recognition rate of steps. Barth [17] developed a mobile system to rate gait impairment. The authors used a multidimensional subsequence DTW method to extract single strides from gait tests and free walking. ...
The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson’s disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD.
... The Fast Fourier transformation is used in [8] to detect the freezing phases. Newest research again uses DTW e.g. for the segmentation of gait sequences [9] and for recognition of asymmetry in gait [10]. For the detection of freezing phases, which occur especially during turns, the turn was analyzed [8] [9]. ...
... In this paper, the stage of the movement disorder is not to be determined on the basis of individual characteristics such as asymmetry or freezing, but rather as the combination of all single disorders. The IMU sensors have been mounted on the shoe [7] or on the ankle [8] [9] [10]. In this case, the sensor may slip during walking. ...
... One of the most important feature sets considered in modeling motor impairments of PD patients are those based on the kinematic analysis of the gait process [44]. Conventional kinematic features are based on the duration, length and velocity of the strides. ...
Aim: This paper introduces Apkinson, a mobile application for motor evaluation and monitoring of Parkinson’s disease patients. Materials & methods: The App is based on previously reported methods, for instance, the evaluation of articulation and pronunciation in speech, regularity and freezing of gait in walking, and tapping accuracy in hand movement. Results: Preliminary experiments indicate that most of the measurements are suitable to discriminate patients and controls. Significance is evaluated through statistical tests. Conclusion: Although the reported results correspond to preliminary experiments, we think that Apkinson is a very useful App that can help patients, caregivers and clinicians, in performing a more accurate monitoring of the disease progression. Additionally, the mobile App can be a personal health assistant.
Our understanding of Parkinson’s disease (PD), its symptoms and diagnosis have been expanded considerably since its formal description by James Parkinson two centuries ago [Prze17]. However, this common neurodegenerative disorder has still been a threat to the health and well-being of patients and an economic burden, since a complete treatment is still a formidable barrier. Impaired gait is one of the most characteristic symptoms in Parkinson’s disease. Assessment of movement impairments forms a basis for diagnosis, evaluation of the disease progress and the evaluation of therapeutic interventions. The emergence of wearable technologies has permitted the development of mobile systems for gait analysis. This technology enables us to record large amounts of patients’ data not only during clinical visits but also outside clinics. Data driven methods hold the potential to analyze the large volume of data to provide an objective disease assessment, improve current approaches to manage disease progression, and monitor patients outside clinics. This thesis aims to leverage data driven methods for the development of an objective gait assessment using mobile gait analysis systems. The present thesis answers three main open questions in this domain: development and comparison of four widely used data driven methods for gait segmentation, analysis of turning for on-shoe wearable sensors and interpretable classification of motor impairments. Regarding segmentation of gait sequence to individual strides, three existing segmen- tation methods are implemented and validated for PD population. For this applica- tion, a novel segmentation method is also introduced and implemented for the first time. These methods are evaluated on two data sets with different levels of data heterogeneity. This contribution presents a fair comparison of segmentation methods on an identical data set. Segmenting gait sequences is the first step in the following steps of research: turning analysis and assessing motor impairments in PD. Further, turning deficits are examined using an on-shoe mobile gait analysis system. A method is introduced for isolation of turning from the whole gait sequence based on the statistics of turning angles between two consecutive strides. Correlation of turn-derived spatio-temporal features with two widely used clinical scales is examined. This is a proof-of-concept for the feasibility of using on-shoe mobile gait analysis systems for turning analysis in PD. Turn-derived spatio-temporal features, then, are used in the next contribution. Finally, spatio-temporal features computed from straight walking as well as turning are used for the classification of different levels of motor impairments. Gaussian pro- cesses, a probabilistic machine learning method, is introduced for the first time for this application. The method provides the classification output as well as an explicit uncertainty measure, which captures the confidence of the method in the estimated output. The measure of uncertainty is particularly important in cases when the data set is small and noisy. A discussion regarding the properties of this type of data driven method and its evaluation is presented. To conclude, the present thesis centers on the development of data driven methods for objective assessment of gait in Parkinson’s disease. The works mentioned above contribute to the early diagnosis, evaluation of disease progression, assessment of therapeutic interventions and insights for long-term monitoring of patients outside clinics. Understanding the potentials and pitfalls of data driven methods in gait analysis leads to deeper insight into Parkinson’s disease and opens new doors for the disease management.
Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson’s disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson’s disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson’s disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects’ preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson’s disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson’s disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson’s disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson’s disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.
Macular degeneration is the third leading cause of blindness worldwide and the leading cause of blindness in the developing world. The analysis of gait parameters can be used to assess the influence of macular degeneration on gait. This study examines the effect of macular degeneration on gait using inertial sensor based 3D spatio-temporal gait parameters. We acquired gait data from 21 young and healthy subjects during a 40 m obstacle walk. All subjects had to perform the gait trial with and without macular degeneration simulation glasses. The order of starting with or without glasses alternated between each subject in order to test for training effects. Multiple 3D spatio-temporal gait parameters were calculated for the normal vision as well as the impaired vision groups. The parameters trial time, stride time, stride time coefficient of variation (CV), stance time, stance time CV, stride length, cadence, gait velocity and angle at toe off showed statistically significant differences between the two groups. Training effects were visible for the trials which started without vision impairment. Inter-group differences in the gait pattern occurred due to an increased sense of insecurity related with the loss of visual acuity from the simulation glasses. In summary, we showed that 3D spatio-temporal gait parameters derived from inertial sensor data are viable to detect differences in the gait pattern of subjects with and without a macular degeneration simulation. We believe that this study provides the basis for an in-depth analysis regarding the impact of macular degeneration on gait.
For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry
Objective:
Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase.
Methods:
We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions.
Results:
Even though best results are achieved with strides defined from midstance to midstance with average accuracy and precision of , performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by .
Conclusion:
Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset.
Significance:
With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.