6,143 research outputs found
Ambient health monitoring: the smartphone as a body sensor network component
Inertial measurement units used in commercial body sensor networks (e.g. animation suits) are inefficient, difficult to use and expensive when adapted for movement science applications concerning medical and sports science. However, due to advances in micro-electro mechanical sensors, these inertial sensors have become ubiquitous in mobile computing technologies such as smartphones. Smartphones generally use inertial sensors to enhance the interface usability. This paper investigates the use of a smartphoneâs inertial sensing capability as a component in body sensor networks. It discusses several topics centered on inertial sensing: body sensor networks, smartphone networks and a prototype framework for integrating these and other heterogeneous devices. The proposed solution is a smartphone application that gathers, processes and filters sensor data for the purpose of tracking physical activity. All networking functionality is achieved by Skeletrix, a framework for gathering and organizing motion data in online repositories that are conveniently accessible to researchers, healthcare professionals and medical care workers
Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist
In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91â99% for healthy subjects and 70â85% for stroke patients
Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers
In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the humanarm (extension, flexion and rotation of the forearm) involved in âmaking-a-cup-of-teaâ, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier
A personalized rehabilitation system based on wireless motion capture sensors
We live in an aging society, an issue that will be exacerbated in the coming decades, due to low birth rates
and increasing life expectancy. With the decline in physical and cognitive functions with age, it is of the
utmost importance to maintain regular physical activity,in order to preserve an individualâs mobility, motor
capabilities and coordination. Within this context, thispaper describes the development of a wireless sensor
network and its application in a human motion capturesystem based on wearable inertial and magnetic
sensors. The goal is to enable, through continuous real-time monitoring, the creation of a personalized
home-based rehabilitation system for the elderly population and/or injured people. Within this system, the
user can benefit from an assisted mode, in which their movements can be compared to a reference motion
model of the same movements, resulting in visual feedback alerts given by the application. This motion
model can be created previously, in a âlearning phaseâ, under supervision of a caregiver.Fundação para a CiĂȘncia e a Tecnologia (FCT
Questioning Classic Patient Classification Techniques in Gait Rehabilitation: Insights from Wearable Haptic Technology
Classifying stroke survivors based on their walking abilities is an important part of the gait rehabilitation process. It can act as powerful indicator of function and prognosis in both the early days after a stroke and long after a survivor receives rehabilitation. This classification often relies solely on walking speed; a quick and easy measure, with only a stopwatch needed. However, walking speed may not be the most accurate way of judging individualâs walking ability. Advances in technology mean we are now in a position where ubiquitous and wearable technologies can be used to elicit much richer measures to characterise gait. In this paper we present a case study from one of our studies, where within a homogenous group of stroke survivors (based on walking speed classification) important differences in individual results and the way they responded to rhythmic haptic cueing were identified during the piloting of a novel gait rehabilitation technique
A telerehabilitation system based on wireless motion capture sensors
The constant growth of the elderly population in the world creates new challenges and opportunities in health care systems. New technological solutions have to be found in order to meet the needs and demands of our aging society. The welfare and quality of life of the elderly population must be a priority. Continuous physical activity will play an important role, due to the increase of the retirement age. However, physiotherapy can be expensive, even when the desire movements are autonomous and simple, also requires people to move to rehabilitation centres. Within this context, this paper describes the development and preliminary tests of a wireless sensor network, based on wearable inertial and magnetic sensors, applied to the capture of human motion. This will enable a personalized home-based rehabilitation system for the elderly or people in remote physical locations.Project âAAL4ALLâ, co-financed by the European Community Fund FEDER through COMPETE â Programa Operacional Factores de Competitividade (POFC).FCT â Foundation for Science and Technology â Lisbon, Portugal, through project PEst-C/CTM/LA0025/2013
Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios
This paper presents our experience on a real case of applying an indoor localization system formonitoringolderadultsintheirownhomes. Sincethesystemisdesignedtobeusedbyrealusers, therearemanysituationsthatcannotbecontrolledbysystemdevelopersandcanbeasourceoferrors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task
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