3,251 research outputs found

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

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    © 2015 Massé et al.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

    The Emerging Nature of Participation in Multispecies Interaction Design

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    Interactive technology has become integral part of daily life for both humans and animals, with animals often interacting with technologized environments on behalf of humans. For some, animals' participation in the design process is essential to design technology that can adequately support their activities. For others, animals' inability to understand and control design activities inevitably stands in the way of multispecies participatory practices. Here, we consider the essential elements of participation within interspecies interactions and illustrate its emergence, in spite of contextual constraints and asymmetries. To move beyond anthropomorphic notions of participation, and consequent anthropocentric practices, we propose a broader participatory model based on indexical semiosis, volition and choice; and we highlight dimensions that could define inclusive participatory practices more resilient to the diversity of understandings and goals among part-taking agents, and better able to account for the contribution of diverse, multispecies agents in interaction design and beyond

    Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach

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    International audienceRecent years have witnessed the rapid development of human activity recognition (HAR) based on werable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this paper, we propose a novel method based on the ensemble learning to boost the performance of these machine learning methods for HAR

    Developing novel temperature sensing garments for health monitoring applications

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    Embedding temperature sensors within textiles provides an easy method for measuring skin temperature. Skin temperature measurements are an important parameter for a variety of health monitoring applications, where changes in temperature can indicate changes in health. This work uses a temperature sensing yarn, which was fully characterized in previous work, to create a series of temperature sensing garments: armbands, a glove, and a sock. The purpose of this work was to develop the design rules for creating temperature sensing garments and to understand the limitations of these devices. Detailed design considerations for all three devices are provided. Experiments were conducted to examine the effects of contact pressure on skin contact temperature measurements using textile-based temperature sensors. The temperature sensing sock was used for a short user trial where the foot skin temperature of five healthy volunteers was monitored under different conditions to identify the limitations of recording textile-based foot skin temperature measurements. The fit of the sock significantly affected the measurements. In some cases, wearing a shoe or walking also heavily influenced the temperature measurements. These variations show that textile-based foot skin temperature measurements may be problematic for applications where small temperature differences need to be measured

    A new multisensor software architecture for movement detection: Preliminary study with people with cerebral palsy

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    A five-layered software architecture translating movements into mouse clicks has been developed and tested on an Arduino platform with two different sensors: accelerometer and flex sensor. The archi-tecture comprises low-pass and derivative filters, an unsupervised classifier that adapts continuously to the strength of the user's movements and a finite state machine which sets up a timer to prevent in-voluntary movements from triggering false positives. Four people without disabilities and four people with cerebral palsy (CP) took part in the experi-ments. People without disabilities obtained an average of 100% and 99.3% in precision and true positive rate (TPR) respectively and there were no statistically significant differences among type of sensors and placement. In the same experiment, people with disabilities obtained 97.9% and 100% in precision and TPR respectively. However, these results worsened when subjects used the system to access a commu-nication board, 89.6% and 94.8% respectively. With their usual method of access-an adapted switch- they obtained a precision and TPR of 86.7% and 97.8% respectively. For 3-outof- 4 participants with disabilities our system detected the movement faster than the switch. For subjects with CP, the accelerometer was the easiest to use because it is more sensitive to gross motor motion than the flex sensor which requires more complex movements. A final survey showed that 3-out-of-4 participants with disabilities would prefer to use this new technology instead of their tra-ditional method of access
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