Mobile quantification and therapy course tracking for gait rehabilitation

Alcaraz, Javier Conte; Moghaddamnia, Sanam; Peissig, Jürgen

doi:10.1109/icdsp.2017.8096106

Cited by 2 publications

(2 citation statements)

References 11 publications

(12 reference statements)

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“…The kinematic gait data are transmitted by Bluetooth to an Android application, which we developed for this purpose. The received data is feed to a multi-sensor fusion algorithm, where the lower body joint angles in the sagittal plane are estimated from the kinematic signals of the four IMUs and the gait data are analyzed according to [23]. Figure 2 shows the joint angles estimated with the kinematic gait data of the IMUs.…”

Section: Wearable Sensor Systemmentioning

confidence: 99%

Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Alcaraz

Moghaddamnia

Peissig

2021

EURASIP J. Adv. Signal Process.

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Reliability and user compliance of the applied sensor system are two key issues of digital healthcare and biomedical informatics. For gait assessment applications, accurate joint angle measurements are important. Inertial measurement units (IMUs) have been used in a variety of applications and can also provide significant information on gait kinematics. However, the nonlinear mechanism of human locomotion results in moderate estimation accuracy of the gait kinematics and thus joint angles. To develop “digital twins” as a digital counterpart of body lower limb joint angles, three-dimensional gait kinematic data were collected. This work investigates the estimation accuracy of different neural networks in modeling lower body joint angles in the sagittal plane using the kinematic records of a single IMU attached to the foot. The evaluation results based on the root mean square error (RMSE) show that long short-term memory (LSTM) networks deliver superior performance in nonlinear modeling of the lower limb joint angles compared to other machine learning (ML) approaches. Accordingly, deep learning based on the LSTM architecture is a promising approach in modeling of gait kinematics using a single IMU, and thus can reduce the required physical IMUs attached on the subject and improve the practical application of the sensor system.

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Section: Wearable Sensor Systemmentioning

confidence: 99%

Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Alcaraz

Moghaddamnia

Peissig

2021

EURASIP J. Adv. Signal Process.

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“…Sensors are increasingly pervasive in all aspects of human life. They are widely used in health related applications as already mentioned (rehab, fitness, elderly care, occupational safety) as well as in “smart” cities and homes, tracking of consumer behavior (retail, tourism) or in the social signal processing community (Vinciarelli et al, 2009; Imani et al, 2016; Alcaraz et al, 2017; Goonawardene et al, 2017; Jiang et al, 2017; Oosterlinck et al, 2017). Out of the considerable variety of sensor types and their divergent application fields, the present article concentrates on a relatively well-defined sub-set, namely wearable sensors used in organizational research.…”

Section: A Framework For Conceptualizing Sensor Datamentioning

confidence: 99%

Using Sensors in Organizational Research—Clarifying Rationales and Validation Challenges for Mixed Methods

et al. 2019

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Sensor-based data are becoming increasingly widespread in social, behavioral, and organizational sciences. Far from providing a neutral window on “reality,” sensor-based big-data are highly complex, constructed data sources. Nevertheless, a more systematic approach to the validation of sensors as a method of data collection is lacking, as their use and conceptualization have been spread out across different strands of social-, behavioral-, and computer science literature. Further debunking the myth of raw data, the present article argues that, in order to validate sensor-based data, researchers need to take into account the mutual interdependence between types of sensors available on the market, the conceptual (construct) choices made in the research process, and the contextual cues. Sensor-based data in research are usually combined with additional quantitative and qualitative data sources. However, the incompatibility between the highly granular nature of sensor data and the static, a-temporal character of traditional quantitative and qualitative data has not been sufficiently emphasized as a key limiting factor of sensor-based research. It is likely that the failure to consider the basic quality criteria of social science measurement indicators more explicitly may lead to the production of insignificant results, despite the availability of high volume and high-resolution data. The paper concludes with recommendations for designing and conducting mixed methods studies using sensors.

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Mobile quantification and therapy course tracking for gait rehabilitation

Cited by 2 publications

References 11 publications

Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Using Sensors in Organizational Research—Clarifying Rationales and Validation Challenges for Mixed Methods

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