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Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients.

Massé F, Gonzenbach RR, Arami A, Paraschiv-Ionescu A, Luft AR, Aminian K - J Neuroeng Rehabil (2015)

Bottom Line: The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints.The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation.The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne, Station 11, 1015, Lausanne, Switzerland. fabien.masse@epfl.ch.

ABSTRACT

Background: Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients' mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor.

Methods: Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH).

Results: The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation.

Conclusion: The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier.

No MeSH data available.


Related in: MedlinePlus

Classification of body elevation. a Decision tree for the classification of body elevation / b Example of an activity involving a large elevation (Elevator Down)
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Fig3: Classification of body elevation. a Decision tree for the classification of body elevation / b Example of an activity involving a large elevation (Elevator Down)

Mentions: The BP signal was first converted to altitude (Alt) using the barometric formula [33] then the pattern of elevation was enhanced using a sinusoidal fitting model similar to model used in STS detection [28]. The sinus fitting function (SAlt) was modeled as follows:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \begin{array}{c}\hfill {S}_{Alt}(t)={\Delta}_{Alt}*E\left(\frac{t-Al{t}_{delay}}{Al{t}_{duration}}\right)+Al{t}_{drift}*t+Al{t}_{offset}\ \hfill \\ {}\hfill with\ E(t)\left\{\begin{array}{ll}-1/2\hfill & if\ t\le -1/2\hfill \\ {}1/2* \sin \left(\pi t\right),\hfill & if-1/2<t\le 1/2\hfill \\ {}+1/2\hfill & t>1/2\hfill \end{array}\right.\hfill \end{array} $$\end{document}SAltt=ΔAlt*Et−AltdelayAltduration+Altdrift*t+AltoffsetwithEt−1/2ift≤−1/21/2*sinπt,if−1/2<t≤1/2+1/2t>1/2where the model parameters ∆Alt, Altduration,, Altoffset,Altdrift, Altdelay are depicted in Fig. 3. They represent over the course of the activity the change in altitude, the duration of the part of the activity that involves a potential elevation change, the potential elevation drift and the elevation offset, respectively. For each activity, the model was obtained from the altitude data (over the duration of the activity being processed) using the “Trust-region reflective” optimization procedure [34].Fig. 3


Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients.

Massé F, Gonzenbach RR, Arami A, Paraschiv-Ionescu A, Luft AR, Aminian K - J Neuroeng Rehabil (2015)

Classification of body elevation. a Decision tree for the classification of body elevation / b Example of an activity involving a large elevation (Elevator Down)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4549072&req=5

Fig3: Classification of body elevation. a Decision tree for the classification of body elevation / b Example of an activity involving a large elevation (Elevator Down)
Mentions: The BP signal was first converted to altitude (Alt) using the barometric formula [33] then the pattern of elevation was enhanced using a sinusoidal fitting model similar to model used in STS detection [28]. The sinus fitting function (SAlt) was modeled as follows:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \begin{array}{c}\hfill {S}_{Alt}(t)={\Delta}_{Alt}*E\left(\frac{t-Al{t}_{delay}}{Al{t}_{duration}}\right)+Al{t}_{drift}*t+Al{t}_{offset}\ \hfill \\ {}\hfill with\ E(t)\left\{\begin{array}{ll}-1/2\hfill & if\ t\le -1/2\hfill \\ {}1/2* \sin \left(\pi t\right),\hfill & if-1/2<t\le 1/2\hfill \\ {}+1/2\hfill & t>1/2\hfill \end{array}\right.\hfill \end{array} $$\end{document}SAltt=ΔAlt*Et−AltdelayAltduration+Altdrift*t+AltoffsetwithEt−1/2ift≤−1/21/2*sinπt,if−1/2<t≤1/2+1/2t>1/2where the model parameters ∆Alt, Altduration,, Altoffset,Altdrift, Altdelay are depicted in Fig. 3. They represent over the course of the activity the change in altitude, the duration of the part of the activity that involves a potential elevation change, the potential elevation drift and the elevation offset, respectively. For each activity, the model was obtained from the altitude data (over the duration of the activity being processed) using the “Trust-region reflective” optimization procedure [34].Fig. 3

Bottom Line: The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints.The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation.The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne, Station 11, 1015, Lausanne, Switzerland. fabien.masse@epfl.ch.

ABSTRACT

Background: Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients' mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor.

Methods: Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH).

Results: The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation.

Conclusion: The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier.

No MeSH data available.


Related in: MedlinePlus