OBJECTIVES: To study sensor monitoring (use of a sensor network placed in the home environment to observe individuals' daily functioning (activities of daily living and instrumental activities of daily living)) as a method to measure and support daily functioning for older people living independently at home.DESIGN: Systematic review.SETTING: Participants' homes.PARTICIPANTS: Community-dwelling individuals aged 65 and older.MEASUREMENTS: A systematic search in PubMed, Embase, PsycINFO, INSPEC, and The Cochrane Library was performed for articles published between 2000 and October 2012. All study designs, studies that described the use of wireless sensor monitoring to measure or support daily functioning for independently living older people, studies that included community-dwelling individuals aged 65 and older, and studies that focused on daily functioning as a primary outcome measure were included.RESULTS: Seventeen articles met the inclusion criteria. Nine studies used sensor monitoring solely as a method for measuring daily functioning and detecting changes in daily functioning. These studies focused on the technical investigation of the sensor monitoring method used. The other studies investigated clinical applications in daily practice. The sensor data could enable healthcare professionals to detect alert conditions and periods of decline and could enable earlier intervention, although limited evidence of the effect of interventions was found in these studies because of a lack of high methodological quality.CONCLUSION: Studies on the effectiveness of sensor monitoring to support people in daily functioning remain scarce. A road map for further development is proposed.
Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
PURPOSE: The early detection of a decline in daily functioning of independently living older people can aid health care professionals in providing preventive interventions. To monitor daily activity patterns and, thereby detect a decline in daily functioning, new technologies, such as sensors can be placed in the home environment. The purpose of this qualitative study was to determine the perspectives of older people regarding the use of sensor monitoring in their daily lives.DESIGN AND METHODS: We conducted indepth, semistructured interviews with 11 persons between 68 and 93 years who had a sensor monitoring system installed in their home. The data were analyzed using Interpretative Phenomenological Analysis.RESULTS: The interviewed older persons positively valued sensor monitoring and indicated that the technology served as a strategy to enable independent living. The participants perceived that the system contributed to their sense of safety as an important premise for independent living. Some of the participants stated that it helped them to remain active. The potential privacy violation was not an issue for the participants. The participants considered that health care professionals' continuous access to their sensor data and use of the data for their safety outweighed the privacy concerns.IMPLICATIONS: These results provide new evidence that older persons experience sensor monitoring as an opportunity or strategy that contributes to independent living and that does not disturb their natural way of living. Based on this study, the development of new strategies to provide older people with access to their sensor data must be further explored.
De glastuinbouw in Nederland is wereldwijd toonaangevend en loopt voorop in automatisering en data-gedreven bedrijfsvoering. Voor de data-gedreven teelt wordt, naast het monitoren van de kas-parameters ook het monitoren van gewasparameters steeds meer gevraagd. De sector is daarbij vooral geïnteresseerd in niet-destructieve, contactloze en persoonsonafhankelijk monitoring van gewassen. Optische sensortechnologie, zoals spectrale afbeeldingstechnologie, kan veel waardevolle informatie opleveren over de staat van een gewas of vrucht, bijvoorbeeld over het suikergehalte, maar ook de aanwezigheid van plantziektes of insecten. Echter is dit vaak een te kostbare oplossing voor zowel de technologiebedrijven die oplossingen leveren als voor de telers zelf. In dit project onderzoeken wij de mogelijkheid om spectrale beeldvorming tegen lagere kosten te realiseren. Het beoogde resultaat is een prototype van een instrument dat tegen lage kosten met spectrale beeldvorming een of meerdere gewaseigenschappen kan kwantificeren. Realisatie van dit prototype heeft een sterke Fotonica-component (expertise Haagse Hogeschool) maakt gebruik van Machine Learning (expertise perClass) en is bedoeld voor toepassing op scout robots in de glastuinbouw (expertise Mythronics). Een betaalbare oplossing betekent in potentie voor de teler een betere controle over kwaliteit van het gewas en automatisering voor detectie van ziekte-uitbraken. Bij een succesvol prototype kan deze innovatie leiden tot betere voedselkwaliteit en minder verspilling in de glastuinbouw.
Agricultural/horticultural products account for 9% of Dutch gross domestic product. Yearly expansion of production involves major challenges concerning labour costs and plant health control. For growers, one of the most urgent problems is pest detection, as pests cause up to 10% harvest loss, while the use of chemicals is increasingly prohibited. For consumers, food safety is increasingly important. A potential solution for both challenges is frequent and automated pest monitoring. Although technological developments such as propeller-based drones and robotic arms are in full swing, these are not suitable for vertical horticulture (e.g. tomatoes, cucumbers). A better solution for less labour intensive pest detection in vertical crop horticulture, is a bio-inspired FW-MAV: Flapping Wings Micro Aerial Vehicle. Within this project we will develop tiny FW-MAVs inspired by insect agility, with high manoeuvrability for close plant inspection, even through leaves without damage. This project focusses on technical design, testing and prototyping of FW-MAV and on autonomous flight through vertically growing crops in greenhouses. The three biggest technical challenges for FW-MAV development are: 1) size, lower flight speed and hovering; 2) Flight time; and 3) Energy efficiency. The greenhouse environment and pest detection functionality pose additional challenges such as autonomous flight, high manoeuvrability, vertical take-off/landing, payload of sensors and other equipment. All of this is a multidisciplinary challenge requiring cross-domain collaboration between several partners, such as growers, biologists, entomologists and engineers with expertise in robotics, mechanics, aerodynamics, electronics, etc. In this project a co-creation based collaboration is established with all stakeholders involved, integrating technical and biological aspects.
Gebruik van sensoren en data voor het monitoren van welzijn en gezondheid van mens en dier, raakt steeds meer ingeburgerd. Ook voor de paardenhouderij is het interessant om met behulp van sensoren de gezondheid en het welzijn van de paarden te volgen en in geval van ziekte of stress preventief te kunnen handelen. In tegenstelling tot het ruime aanbod voor de veehouderij, zijn er voor paarden nog weinig of geen sensoren beschikbaar voor gezondheidsmonitoring. In dit project zullen halsbanden voor paarden worden ontwikkeld met activiteitssensoren (accelerometers), die gedragsdata verzamelen. Deze data worden vertaald in informatie over het normale en afwijkende gedrag van de paarden. Activiteit en gedrag worden gekoppeld aan gezondheid en het welzijn van het paard. Doel is om een systeem te ontwikkelen waarbij gezondheid en welzijn van de paarden gemonitord wordt met behulp van deze sensor, en waarbij de eigenaar gewaarschuwd wordt wanneer veranderingen in gedrag optreden die voorspellend zijn voor ziekte, stress of afwijkingen.
