Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping

Steinmetzer, Tobias; Bönninger, Ingrid; Priwitzer, Barbara; Reinhardt, F; Reckhardt, Markus; Erk, Dorela; Travieso, Carlos M.

doi:10.1109/iwobi.2018.8464203

Cited by 10 publications

(17 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was the ratio of predicted non-MD persons in all test data where healthy persons were present; see Equation (8). Precision was the proportion of correctly predicted MD to all MD; see Equation (9). Accuracy was the ratio of all correctly recognized MD and no MD to all test data; see Equation (10).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

et al. 2019

Self Cite

View full text Add to dashboard Cite

Due to increasing life expectancy, the number of age-related diseases with motor dysfunctions (MD) such as Parkinson’s disease (PD) is also increasing. The assessment of MD is visual and therefore subjective. For this reason, many researchers are working on an objective evaluation. Most of the research on gait analysis deals with the analysis of leg movement. The analysis of arm movement is also important for the assessment of gait disorders. This work deals with the analysis of the arm swing by using wearable inertial sensors. A total of 250 records of 39 different subjects were used for this task. Fifteen subjects of this group had motor dysfunctions (MD). The subjects had to perform the standardized Timed Up and Go (TUG) test to ensure that the recordings were comparable. The data were classified by using the wavelet transformation, a convolutional neural network (CNN), and weight voting. During the classification, single signals, as well as signal combinations were observed. We were able to detect MD with an accuracy of 93.4% by using the wavelet transformation and a three-layer CNN architecture.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Most of the research on gait analysis deals with the analysis of leg motion [4][5][6][7][8][9][10]. However, the analysis of the arm movement is also important for the assessment of a gait disorder.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…To measure and store the time series of gait wearable containing microcontrollers in combination with IMU sensors are often used [6][7][8][9][10][11][16][17][18][19][20][21][22] . In most cases, the motion of the lower extremities is measured [6][7][8][9][10][11]22] . Thereby conclusions can be made about the stride length, cadence, stride duration, gait phases, and symmetry [6,10,11,23] .…”

Section: Symmetrymentioning

confidence: 99%

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Steinmetzer¹,

Wilberg²,

Bönninger³

et al. 2020

Microprocessors and Microsystems

Self Cite

View full text Add to dashboard Cite

Gait deviations such as asymmetry are one of the characteristic symptoms of motor dysfunctions that contribute to the risk of falls. Our objective is to measure gait abnormalities such as asymmetry of the lower limbs in order to evaluate the diagnosis more objectively. For the measurement we use inertial measurement unit (IMU) sensors and force sensors, which are integrated in wristbands and insoles. To extend the battery life of wearable devices, we only save data of the activity gait within the wearables. Therefore we perform activity recognition with a smartphone. Using convolutional neural network (CNN) we achieved an accuracy of 94.7 % of the activity gait recognition. Before recording we synchronize the wearable sensors and reach a maximum latencies of 3 ms. Before the analysis of the symmetry we detect the strides by using a CNN with an accuracy of 98.8 %. For the symmetry evaluation we used dynamic time warping (DTW). The DTW enables us to calculate symmetry of the complete time series of human gait.

show abstract

“…Additional sensor data such as pressure measurements could be used to further improve the method. This has also been proposed in a study by Steinmetzer et al [31]. An alternative approach to DTW can be Hidden Markov Model (HMM) for assessing the quality of the movements as presented by Rybarczyk et al [32].…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease

2020

View full text Add to dashboard Cite

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.

show abstract

Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping

Cited by 10 publications

References 9 publications

Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease

Contact Info

Product

Resources

About