A Smartphone Application Suite for Assessing Mobility

Madhushri, Priyanka; Dzhagaryan, Armen; Jovanov, Emil; Milenkovic, Aleksandar

doi:10.1109/embc.2016.7591389

Cited by 18 publications

(21 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gait function was assessed with a -timed up and go‖ (TUG) test using a smartphone application (Madhushri et al, 2016). TUG results are presented in the respective group tables (Tables 1-3).…”

Section: Participants and Proceduresmentioning

confidence: 99%

Visual self-motion cues are impaired in Parkinson’s disease yet over-weighted during visual-vestibular integration

Yakubovich

Israeli‐Korn

Halperin

et al. 2019

Preprint

View full text Add to dashboard Cite

Background: Parkinson's disease (PD) is prototypically a movement disorder.Although perceptual and motor functions are interdependent, much less is known about perceptual dysfunction in PD. Perceptual deficits can impact activities of daily living, and contribute to motor symptoms, but might go unnoticed if not tested directly. Posture, gait and balance, affected in PD, rely on veridical perception of one's own motion in space. Yet it is unknown whether self-motion perception is impaired in PD. Objectives:To test self-motion perception in PD, separately for visual and vestibular cues (unisensory), and multisensory integration thereof.Methods: Participants (19 early stage PD, 23 age-matched and 20 young adult controls) experienced vestibular (motion platform), visual (optic flow), and combined visual-vestibular self-motion stimuli, and discriminated whether the stimulus headings were rightward or leftward of straight ahead. PD participants and age-matched controls were tested on two visits (PD on and off medication).Results: PD participants had significantly impaired visual self-motion perception, both on and off medication. This deficit correlated significantly with clinical disease severity. By contrast, their vestibular performance was unimpaired. Remarkably, despite impaired visual self-motion perception, PD participants significantly overweighted visual cues during multisensory (visual-vestibular) integration. Conclusions:Self-motion perception is affected already in early stage PD, specifically by impaired visual (vs. vestibular) function, and by suboptimal visualvestibular integration. This may contribute to impaired balance and gait control. Future investigation into this connection might open up new avenues for alternativetherapies to better treat these symptoms. Furthermore, these results may also impact early PD diagnosis and subtyping.

show abstract

Section: Participants and Proceduresmentioning

confidence: 99%

Visual self-motion cues are impaired in Parkinson’s disease yet over-weighted during visual-vestibular integration

Yakubovich

Israeli‐Korn

Halperin

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

Section: Other Diseasesmentioning

confidence: 99%

“…Madhushri et al proposed a smartphone application for assessing flexibility in the aged population using inertial sensors [67]. They also presented a set of applications to evaluate the implementation of the Smart Insole TUG test with older adults, extracting several parameters from the inertial sensors [67]. The parameters extracted include the duration of the TUG test, the sit-to-stand transition, the lean forward phase, the stand-to-sit shift, the prepare-to-sit period, the sit-down phase, and the lift up phase, the total time of walk, the maximum change of trunk angle during the lean forward phase, the maximum angular velocity during the lean forward and the lift up phases, the total number of steps during walking, and before turn [67].…”

Section: Other Diseasesmentioning

confidence: 99%

“…The parameters extracted include the duration of the TUG test, the sit-to-stand transition, the lean forward phase, the stand-to-sit shift, the prepare-to-sit period, the sit-down phase, and the lift up phase, the total time of walk, the maximum change of trunk angle during the lean forward phase, the maximum angular velocity during the lean forward and the lift up phases, the total number of steps during walking, and before turn [67]. The average error for the implementation of the Smart Insole TUG test is around 2% [67].…”

Section: Other Diseasesmentioning

confidence: 99%

See 1 more Smart Citation

Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

et al. 2020

View full text Add to dashboard Cite

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution.

show abstract

“… The data provide a set of data acquired during the performance of the Timed-Up and Go test [1] , [2] , [3] with the sensors available in a mobile [ 4 , 5 ] and a BITalino devices [6] , including accelerometer, magnetometer, Electroencephalography and Electrocardiography sensors; The data is important for the creation of solutions for automatic validation of Timed-Up and Go test, and, as we acquired Electroencephalography and Electrocardiography data, it will allows to the creation of patterns of different diseases [7] , [8] , [9] , [10] for further developments; The acquired data may be used for the recognition of different stages and activities during the Timed-Up and Go test, as well as the identification of diseases with machine learning techniques [10] , [11] , [12] ; The data are valid for the creation of disease patterns associated with movement, cardiac and brain frequency, and other problems related to walking activity, applying different techniques to reduce the artefacts [13] , [14] , [15] . It also allows further research with the sensors available in off-the-shelf mobile devices for further creation of Mobile Health solutions [ 16 , 17 ].…”

Section: Value Of the Datamentioning

confidence: 99%

Data acquisition of timed-up and go test with older adults: accelerometer, magnetometer, electrocardiography and electroencephalography sensors’ data

et al. 2020

View full text Add to dashboard Cite

We present a dataset related to the acquisition of different sensors data during the performance of the Timed-Up and Go test with the mobile device positioned in a waistband for the acquisition of accelerometer and magnetometer data, and a BITalino device positioned in a chest band for the acquisition of Electrocardiography and Electroencephalography for further processing. The data acquired from the BITalino device is acquired simultaneously by a Bluetooth connection with the same mobile application. The data was acquired in five institutions, including Centro Comunitário das Lameiras, Lar Nossa Senhora de Fátima, Centro Comunitário das Minas da Panasqueira, Lar da Misericórdia da Santa Casa da Misericórdia do Fundão, and Lar da Aldeia de Joanes da Santa Casa da Misericórdia do Fundão from Fundão and Covilhã municipalities (Portugal). This article describes the data acquired from a several subjects from the different institutions for the acquisition of accelerometer and magnetometer data, where each person performed the Timed-Up and Go test three times, where each output from the sensors was acquired with a sampling rate of 100 Hz. Related to the data acquired by the sensors connected to the BITalino device, 31 persons performed the different experiments related to the Timed-Up and Go Test. Following the data acquired from Electroencephalography and Electrocardiography sensors, only the data acquired from 14 individuals was considered valid. The data acquired by a BITalino device has a sampling rate of 100 Hz. These data can be reused for testing machine learning methods for the evaluation of the performance of the Timed-Up and Go test with older adults.

show abstract

A Smartphone Application Suite for Assessing Mobility

Cited by 18 publications

References 9 publications

Visual self-motion cues are impaired in Parkinson’s disease yet over-weighted during visual-vestibular integration

Visual self-motion cues are impaired in Parkinson’s disease yet over-weighted during visual-vestibular integration

Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

Data acquisition of timed-up and go test with older adults: accelerometer, magnetometer, electrocardiography and electroencephalography sensors’ data

Contact Info

Product

Resources

About