Abstract
BackgroundStroke 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.MethodsOur 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).ResultsThe 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.ConclusionThe 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.
Highlights
Stroke impacts approximately 17 million people worldwide every year [1]
The hierarchical Fuzzy Inference System (H-FIS) outperformed the FIS-IMU by 3.3 %, the FIS-IMUBP by 1.0 %, and the epoch-based classifier (EPOCH) by 5.6 %. This is mostly due to an improvement of the F-score for the standing activity, consecutive to an improvement of Positive Predictive Value (PPV) (+3.5 %) with respect to FIS-IMUBP and a 45.1 % drop of sensitivity for the EPOCH (80.6 % for H-FIS vs. 35.5 % for EPOCH)
Body elevation The activity confusion matrices and validation metrics are presented in the Table 7 for comparison between the EPOCS-barometric pressure (BP), the H-FIS, and H-FISnoFIT
Summary
Stroke impacts approximately 17 million people worldwide every year [1]. Post-stroke survivors are mostly affected by mobility impairments, due to ataxia or hemiplegia, and consequences of lesion in the motor cortex following the stroke. Therapeutic decisions are usually based on clinical assessment of motor function using functional tests such as the Berg Balance Scale (BBS) for balance assessment [2] or Timed Up and Go (TUG) for gait and balance evaluation [3], or on patient reports including QoL questionnaires such as the generic Stroke Impact Scale [4] or Stroke-specific Quality of Life [5]. Activity monitoring in everyday life is expected to provide a more comprehensive assessment of physical functioning and QoL of post-stroke patients. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients’ mobility in daily life. Existing approaches based on inertial sensors have limited performance, in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. One possibility is to use additional information from a barometric pressure (BP) sensor
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