Better Physical Activity Classification using Smartphone Acceleration Sensor

Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities

A novel miniaturized biosensor for monitoring atlantic salmon swimming activity and respiratory frequency

Good fish welfare is one of the prerequisites for sustainable aquaculture. Knowing how fish respond to the production conditions would allow us to better understand their biology and to further optimize production. The new miniaturized biosensor AEFishBIT was successfully used to monitor individual physical activity and respiratory frequency of two Mediterranean farmed fish species (gilthead sea bream and European sea bass). In this study, we aimed to test the use of AEFishBIT to monitor the performance of Atlantic salmon under experimental conditions. An adapted tagging procedure for salmon was developed and used to record salmon responses to handling and changing light conditions. AEFishBIT data showed a stabilization of swimming activity 8 h after handling and tagging with changes in activity or activity and respiratory quotient after changes in light intensity regimes. The results of this study supported the use of AEFishBIT to generate new behavior insights in Atlantic salmon culture.publishedVersio

Ultra-Low Power Sensor Devices for Monitoring Physical Activity and Respiratory Frequency in Farmed Fish

Integration of technological solutions aims to improve accuracy, precision and repeatability in farming operations, and biosensor devices are increasingly used for understanding basic biology during livestock production. The aim of this study was to design and validate a miniaturized tri-axial accelerometer for non-invasive monitoring of farmed fish with re-programmable schedule protocols. The current device (AE-FishBIT v.1s) is a small (14 mm × 7 mm × 7 mm), stand-alone system with a total mass of 600 mg, which allows monitoring animals from 30 to 35 g onwards. The device was attached to the operculum of gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax) juveniles for monitoring their physical activity by measurements of movement accelerations in x- and y-axes, while records of operculum beats (z-axis) served as a measurement of respiratory frequency. Data post-processing of exercised fish in swimming test chambers revealed an exponential increase of fish accelerations with the increase of fish speed from 1 body-length to 4 body-lengths per second, while a close relationship between oxygen consumption (MO2) and opercular frequency was consistently found. Preliminary tests in free-swimming fish kept in rearing tanks also showed that device data recording was able to detect changes in daily fish activity. The usefulness of low computational load for data pre-processing with on-board algorithms was verified from low to submaximal exercise, increasing this procedure the autonomy of the system up to 6 h of data recording with different programmable schedules. Visual observations regarding tissue damage, feeding behavior and circulating levels of stress markers (cortisol, glucose, and lactate) did not reveal at short term a negative impact of device tagging. Reduced plasma levels of triglycerides revealed a transient inhibition of feed intake in small fish (sea bream 50–90 g, sea bass 100–200 g), but this disturbance was not detected in larger fish. All this considered together is the proof of concept that miniaturized devices are suitable for non-invasive and reliable metabolic phenotyping of farmed fish to improve their overall performance and welfare. Further work is underway for improving the attachment procedure and the full device packaging

Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data

peer-reviewedPrecision Technologies are emerging in the context of livestock farming to improve management practices and the health and welfare of livestock through monitoring individual animal behaviour. Continuously collecting information about livestock behaviour is a promising way to address several of these target areas. Wearable accelerometer sensors are currently the most promising system to capture livestock behaviour. Accelerometer data should be analysed properly to obtain reliable information on livestock behaviour. Many studies are emerging on this subject, but none to date has highlighted which techniques to recommend or avoid. In this paper, we systematically review the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants. Our review is based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies, namely (1) Data Collection, (2) Data Pre-Processing and (3) Model Development, with different techniques used at each of the 3 steps. The aim of this review is thus to (i) summarise the predictive performance of models and point out the main limitations of the 3-step methodology, (ii) make recommendations on a methodological blueprint for future studies and (iii) propose lines to explore in order to address the limitations outlined. This review shows that the 3-step methodology ensures that several major ruminant behaviours can be reliably predicted, such as grazing/eating, ruminating, moving, lying or standing. However, the areas faces two main limitations: (i) Most models are less accurate on rarely observed or transitional behaviours, behaviours may be important for assessing health, welfare and environmental issues and (ii) many models exhibit poor generalisation, that can compromise their commercial use. To overcome these limitations we recommend maximising variability in the data collected, selecting pre-processing methods that are appropriate to target behaviours being studied, and using classifiers that avoid over-fitting to improve generalisability. This review presents the current situation involving the use of sensors as valuable tools in the field of behaviour recording and contributes to the improvement of existing tools for automatically monitoring ruminant behaviour in order to address some of the issues faced by livestock farming

Jerk-based feature extraction for robust activity recognition from acceleration data

vo

Accelerometer validity to measure and classify movement in team sports

 In team sports accelerometers are used to monitor the physical demands of athletic performance. Daniel\u27s research showed that accelerometer accuracy can be improved through filtering. He also showed that the accelerometer can be used to automatically classify the type of movement performed. Further improving the understanding of team sports

An enhanced sensor-based approach for evaluation of a geriatric fall risk in non-ambulatory environments

Jedes Jahr stürzt rund ein Drittel der über 65 Jährigen. Stürze sind die Hauptursache für mittlere bis schwere Verletzungen und damit eine enorme Belastung für das Gesundheitssystem. Eine zeitlich akkurate Sturzrisikobewertung in einer breit akzeptierten und nicht-stigmatisierenden Art und Weise kann zu signifikanten Veränderungen in der Strategie der Sturzprävention führen und damit dazu beitragen, die Anzahl der stürzenden Personen, sowie die Sturzrate zu reduzieren. Die gegenwärtige klinische Evaluierung des Sturzrisikos ist zeitaufwendig und subjektiv. Folglich sind Bewertungen in stationärem Umfeld obstruktiv, oder fokussieren sich ausschließlich auf einmalige, periodische Merkmale der menschlichen Bewegung. Der Fokus dieser Arbeit liegt in der Erforschung und Definition neuer Konzepte zur Beurteilung der Koordination der Extremitäten, der Art des Gehens und der Aufstehvorgänge anhand von Signalen von am Handgelenk getragener Inertial- und Umgebungssensorik. Merkmale im Zeit- und Frequenzraum wurden händisch entwickelt, um daraus Support Vector Maschine -Modelle abzuleiten. Die Modelle beschreiben die physikalische Leistungsfähigkeit einer Person in Form einer objektiven (quantitativen) Sturzrisikobewertung in einem störungsanfälligen häuslichen Umfeld. Für erste Untersuchungszwecke wurde eine Forschungsstudie mit 28 älteren Teilnehmern in einem kontrollierten Umfeld durchgeführt. Darauf aufsetzend wurde eine große Querschnittsstudie mit einer Kohorte von 180 Probanden durchgeführt. Eine sich der Messwoche anschließende sechsmonatige Nachverfolgungsphase wurde zur Validierung der Modelle in die Studie inkludiert. Die Ergebnisse haben einen neuen Prädiktor für akutes Sturzrisiko hervorgebracht. Zusätzlich konnte aufgezeigt werden, dass die Kenntnis der Umgebungsbedingungen relevant sind, um die menschlichen Bewegungen richtig bewerten zu können. Ein innovativer Echtzeitalgorithmus wurde entwickelt, in dem Multi-Sensor-Ansätze fusioniert, sowie auf Bewegung basierende Filter integriert sind. Die Einflüsse der Hand-Abhängigkeit auf die Leistungsfähigkeit des Algorithmus konnten im Rahmen dieser Arbeit untersucht werden. Die Validierung der entwickelten Modelle in allen drei Domänen gegen die Grundwahrheit zeigt eine klinisch relevante Genauigkeit oder zumindest teilweise bessere Ergebnisse gegenüber dem Stand der Technik. Die Studie zeigt die Möglichkeit auf, Einschränkungen klinischer Tests zu bewältigen, sowie in Armbändern integrierte Sensorik sowohl für eine akute, wie auch eine konventionelle Sechsmontasbewertung des Sturzrisikos verlässlich anzuwenden