8,852 research outputs found
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
Micro-doppler-based in-home aided and unaided walking recognition with multiple radar and sonar systems
Published in IET Radar, Sonar and Navigation. Online first 21/06/2016.The potential for using micro-Doppler signatures as a basis for distinguishing between aided and unaided gaits is considered in this study for the purpose of characterising normal elderly gait and assessment of patient recovery. In particular, five different classes of mobility are considered: normal unaided walking, walking with a limp, walking using a cane or tripod, walking with a walker, and using a wheelchair. This presents a challenging classification problem as the differences in micro-Doppler for these activities can be quite slight. Within this context, the performance of four different radar and sonar systems â a 40â
kHz sonar, a 5.8â
GHz wireless pulsed Doppler radar mote, a 10â
GHz X-band continuous wave (CW) radar, and a 24â
GHz CW radar â is evaluated using a broad range of features. Performance improvements using feature selection is addressed as well as the impact on performance of sensor placement and potential occlusion due to household objects. Results show that nearly 80% correct classification can be achieved with 10â
s observations from the 24â
GHz CW radar, whereas 86% performance can be achieved with 5â
s observations of sonar
Gait Velocity Estimation using time interleaved between Consecutive Passive IR Sensor Activations
Gait velocity has been consistently shown to be an important indicator and
predictor of health status, especially in older adults. It is often assessed
clinically, but the assessments occur infrequently and do not allow optimal
detection of key health changes when they occur. In this paper, we show that
the time gap between activations of a pair of Passive Infrared (PIR) motion
sensors installed in the consecutively visited room pair carry rich latent
information about a person's gait velocity. We name this time gap transition
time and show that despite a six second refractory period of the PIR sensors,
transition time can be used to obtain an accurate representation of gait
velocity.
Using a Support Vector Regression (SVR) approach to model the relationship
between transition time and gait velocity, we show that gait velocity can be
estimated with an average error less than 2.5 cm/sec. This is demonstrated with
data collected over a 5 year period from 74 older adults monitored in their own
homes.
This method is simple and cost effective and has advantages over competing
approaches such as: obtaining 20 to 100x more gait velocity measurements per
day and offering the fusion of location-specific information with time stamped
gait estimates. These advantages allow stable estimates of gait parameters
(maximum or average speed, variability) at shorter time scales than current
approaches. This also provides a pervasive in-home method for context-aware
gait velocity sensing that allows for monitoring of gait trajectories in space
and time
Utilising wearable and environmental sensors to identify the context of gait performance in the home
In this paper we describe our work on the development of a multi-sensory deployment within the homes of elderly people prone to falling. The aim of our work is to provide both preventative guidance with regards to environmental hazards, as well as to create rich information context around gait performance, near-falls or falls that do happen so the cause can be diagnosed more thoroughly. We use a gait analysis platform developed at the TRIL Centre, coupled with a SenseCam wearable camera, to identify the activities and the location in the home during walking activities. In addition to this, and to add even more context, we use home energy- monitoring to enhance our understanding of activities and activity patterns in the home. This method could support older people in identifying a key problem and allow the participant to modify their behaviour or environment to limit or prevent future occurrences
Is the timed-up and go test feasible in mobile devices? A systematic review
The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinsonâs disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio
A cane-based low cost sensor to implement attention mechanisms in telecare robots
Telepresence robots have been recently used for
Comprehensive Geriatric Assessment (CGA). Since the robot
can not track a person continuously, there are several strategies
to decide when to check them, from cyclic checks to simple
requests from users and/or caregivers. In order to adapt to the
user needs and condition, it is preferable to perform CGA as
soon as regularities appear. However, this requires detection
of potential issues in users to offer immediate service. In this
work we propose a new low cost force sensor system to detect
userâs condition and attract attention of CGA robots, so they
can perform a full examination on a need basis. The main
advantages of this system are: i) it can be attached to any
standard commercial cane; ii) its power consumption is very
reduced; and iii) it provides continuous information as long as
the user walks. It has been tested with several elderly volunteers
in care facilities. Results have proven that the sensor readings
are indeed correlated with the usersâ condition.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tec
Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer
Among Parkinsonâs disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patientâs treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.Peer ReviewedPostprint (published version
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