Inertial sensors signal processing methods for gait analysis of patients with impaired gait patterns

Đurić-Jovičić, Milica D.

doi:10.2298/bg20121123djuricjovicic

Cited by 3 publications

(6 citation statements)

References 58 publications

(78 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data acquisition is controlled through a user-friendly graphical interface. Wireless communication enables convenient usage of the system in clinical environment, covering the radius of 20 m indoors 22 .…”

Section: Instrumentationmentioning

confidence: 99%

“…In order to provide 3-D movement analysis, we estimated the angles between the index finger and the thumb (finger tapping angle). The developed software employs transformation matrices and introduces biomechanical constraints of tapping movements 22 . Hand orientation or possible changes in position and orientation are irrelevant for the system performance.…”

Section: Signal Processingmentioning

confidence: 99%

See 1 more Smart Citation

Finger and foot tapping sensor system for objective motor assessment

Djurić-Jovičić¹,

Jovičić²,

Radovanović

et al. 2018

VOJNOSANIT PREGL

Self Cite

View full text Add to dashboard Cite

Background/Aim. Finger tapping test is commonly used in neurological examinations as a test of motor performance. The new system comprising inertial and force sensors and custom proprietary software was developed for quantitative estimation and assessment of finger and foot tapping tests. The aim of this system was to provide diagnosis support and objective assessment of motor function. Methods. Miniature inertial sensors were placed on fingertips and used for measuring finger movements. A force sensor was placed on the fingertip of one finger, in order to measure the force during tapping. For foot tapping assessment, an inertial sensor was mounted on the subject's foot, which was placed above a force platform. By using this system, various parameters such as a number of taps, tapping duration, rhythm, open and close speed, the applied force and tapping angle, can be extracted for detailed analysis of a patient's motor performance. The system was tested on 13 patients with Parkinson's disease and 14 healthy controls. Results. The system allowed easy measurement of listed parameters, and additional graphical representation showed quantitative differences in these parameters between neurological patient and healthy subjects. Conclusion. The novel system for finger and foot tapping test is compact, simple to use and efficiently collects patient data. Parameters measured in patients can be compared to those measured in healthy subjects, or among groups of patients, or used to monitor progress of the disease, or therapy effects. Created data and scores could be used together with the scores from clinical tests, providing the possibility for better insight into the diagnosis.

show abstract

Section: Instrumentationmentioning

confidence: 99%

Section: Signal Processingmentioning

confidence: 99%

Finger and foot tapping sensor system for objective motor assessment

Djurić-Jovičić¹,

Jovičić²,

Radovanović

et al. 2018

VOJNOSANIT PREGL

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proposed system, named SENSY was designed and developed within collaboration between the Faculty of Electrical Engineering, University of Belgrade, Serbia and Tecnalia Research Center, San Sebastian, Spain 12 . SENSY hardware comprises six peripheral inertial measurement units (IMUs), one per each leg segment of both legs, and a central PC communication unit connected to a USB port of a remote computer, where signals are monitored and stored (Figure 1).…”

Section: Instrumentation -Wireless Sensor System For Gait Analysismentioning

confidence: 99%

“…In order to provide 3-D gait analysis and trajectory reconstruction, the software employs transformation matrices and combines human locomotion and biomechanical constraints in order to fuse accelerometer and gyroscope data. Polynomial fitting eliminates the drift 12 .…”

Section: Reconstruction Of Gait Trajectorymentioning

confidence: 99%

Quantitative and qualitative gait assessments in Parkinson’s disease patients

et al. 2014

View full text Add to dashboard Cite

The described gait assessment system can be used for detection and categorization of gait disturbances by applying rule-based classification based on stride length, stride time, and frequency of the shank segment movements. The method provides an valuable graphical interface which is easy to interpret and provides clinicians and therapists with valuable information regarding the temporal changes in gait.

show abstract

“…This device could be used as a substitution of computer mouse, providing individuals without hand or forearm to make human-computer control, but also supports other applications of humanmachine interface. Djurić-Jovičić et al (17) developed SENSY system of interactive shoes inertial measurement units (IMU) that measure force distribution and stride parameters in gait analysis. Moodmetric® ring measures electro dermal activity of the skin, as an indicator of sympathetic activation (18) and the appropriate application, estimates the level of physiological and psychological arousal (19).…”

Section: Introductionmentioning

confidence: 99%

Deep learning approaches for human activity recognition using wearable technology

Janković¹,

Savić²,

Novičić³

et al. 2018

Medicinski podmladak

View full text Add to dashboard Cite

The need for long-term monitoring of individuals in their natural environment has initiated the development of a various number of wearable healthcare sensors for a wide range of applications: medical monitoring in clinical or home environments, physical activity assessment of athletes and recreators, baby monitoring in maternity hospitals and homes etc. Neural networks (NN) are data-driven type of modelling. Neural networks learn from experience, without knowledge about the model of phenomenon, but knowing the desired "output" data for the training "input" data. The most promising concept of machine learning that involves NN is the deep learning (DL) approach. The focus of this review is on approaches of DL for physiological activity recognition or human movement analysis purposes, using wearable technologies. This review shows that deep learning techniques are useful tools for health condition prediction or overall monitoring of data, streamed by wearable systems. Despite the considerable progress and wide field of applications, there are still some limitations and room for improvement of DL approaches for wearable healthcare systems, which may lead to more robust and reliable technology for personalized healthcare. Keywords: deep learning, human activity monitoring, human machine interface, wearable sensors, smart sensors, multimodal interface Medicinski podmladak / Medical Youth Janković M. et al. Deep learning approaches for human activity recognition using wearable technology. MedPodml 2018, 69(3):14-24 15Potreba za dugotrajnim praćenjem osoba u njihovom svakodnevnom okruženju inicirala je razvoj velikog broja nosivih senzora (integrisanih u delove garderobe) s različitom primenom, kao što su medicinsko praćenje u kliničkim i kućnim uslovima, procena fizičke aktivnosti sportista i rekreativaca, praćenje beba u porodilištima i kućama i sl. Neuralne mreže (NM) predstavljaju tip modelovanja zasnovan na velikom broju podataka. Ove mreže uče na osnovu iskustva, bez poznavanja modela fenomena, ali znajući šta su željeni "izlazni" podaci za obučavajuće "ulazne" podatke. Koncept mašinskog učenja koji najviše obećava i uključuje NM jeste duboko učenje (DU). Fokus ovog preglednog rada je u pristupima DU u cilju prepoznavanja fizioloških aktivnosti i analize ljudskih pokreta primenom "odevne tehnologije". Ovaj rad pokazuje da su tehnike dubokog učenja korisne alatke za predikciju zdravstvenih stanja ili celokupno praćenje podataka koji se šalju sa "odevnih" senzora. Uprkos značajnom napretku i obećavajućoj oblasti primene, i dalje postoje ograničenja i prostor za unapređenje pristupa DU za "odevne" zdravstvene sisteme koji će dovesti do njihove pouzdane primene i omogućiti personalizovanu zdravstvenu zaštitu.Ključne reči: duboko učenje, praćenje ljudske aktivnosti, čovek-mašina interfejs, odevni senzori, pametni senzori, multimodalni interfejs

show abstract

Inertial sensors signal processing methods for gait analysis of patients with impaired gait patterns

Cited by 3 publications

References 58 publications

Finger and foot tapping sensor system for objective motor assessment

Finger and foot tapping sensor system for objective motor assessment

Quantitative and qualitative gait assessments in Parkinson’s disease patients

Deep learning approaches for human activity recognition using wearable technology

Contact Info

Product

Resources

About