STPT: Spatio-Temporal Polychromatic Trajectory Based Elderly Exercise Evaluation System (original) (raw)
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An Analysis of Human Motion Detection Systems Use During Elder Exercise Routines
Human motion analysis provides motion pattern and body pose estimations. This study integrates computer-vision techniques and explores a markerless human motion analysis system. Using human-computer interaction (HCI) methods and goals, researchers use a computer interface to provide feedback about range of motion to users. A total of 35 adults aged 65 and older perform three exercises in a public gym while human motion capture methods are used. Following exercises, participants are shown processed human motion images captured during exercises on a customized interface. Standardized questionnaires are used to elicit responses from users during interactions with the interface. A matrix of HCI goals (effectiveness, efficiency, and user satisfaction) and emerging themes are used to describe interactions. Sixteen users state the interface would be useful, but not necessarily for safety purposes. Users want better image quality, when expectations are matched satisfaction increases, and unclear meaning of motion measures decreases satisfaction.
Assessing physical performance in free-living older adults with a wearable computer
2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC), 2015
This study investigates the use of a chest-worn wearable computer, the eButton, to assess physical performance of older adults. The Short Physical Performance Battery (SPPB), a standard cliniucal test, is first conducted on older human subjects. Then, a triaxial accelerometer and a triaxial gyroscope within the eButton are utilized to record acceleration and angular velocity of body motion on the same subjects for one week. The sensor data corresponding to walking episodes are segmented and features in the time and frequency domains are extracted. Comparison between these features and the total SPPB scores shows that the sensor data acquired in free-living conditions can be used as indicators of the subjects physical performance.
Sensors
Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects' movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects' movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen's Kappa, corrected kappa, Krippendorff's alpha and Fleiss' kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.
Tracking exercise motions of older adults using contours
In this paper we describe the development of a novel markerless motion capture system and explore its use in documenting elder exercise routines in a health club. This system uses image contour tracking and swarm intelligence methods to track the location of the spine and shoulders during three exercisestreadmill, exercise bike, and overhead lateral pull-down. Validation results show that our method has a mean error of approximately 2 degrees when measuring the angle of the spine or shoulders relative to the horizontal. Qualitative study results demonstrate that our system is capable of providing important feedback about the posture and stability of elders while they are performing exercises. Study participants indicated that feedback from our system would add value to their exercise routines.
Biomedical Engineering, 2003
A new method of physical activity monitoring is presented, which is able to detect body postures (sitting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy ( 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity ( 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking," 8.2% for sitting, and 1.3% for lying.
Evaluating physical function and activity in the elderly patient using wearable motion sensors
EFORT Open Reviews
Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand. Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures. In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts. In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the ‘micro-level’ of activity such as step frequency or sit-stand duration. Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability...
Sensors, 2014
Physical activity is an important determinant of health and well-being in older persons and contributes to their social participation and quality of life. Hence, assessment tools are needed to study this physical activity in free-living conditions. Wearable motion sensing technology is used to assess physical activity. However, there is a lack of harmonisation of validation protocols and applied statistics, which make it hard to compare available and future studies. Therefore, the aim of this paper is to formulate recommendations for assessing the validity of sensor-based activity monitoring in older persons with focus on the measurement of body postures and movements. Validation
Measurement of Human Daily Physical Activity
Obesity, 2003
ZHANG, KUAN, PATRICIA WERNER, MING SUN, † F. XAVIER PI-SUNYER, AND CAROL N. BOOZER. Measurement of human daily physical activity. Obes Res. 2003;11:33-40. Objectives: To validate a new device, Intelligent Device for Energy Expenditure and Activity (IDEEA), for the measurement of duration, frequency, and intensity of various types of human physical activity (PA).
An overview of currently available methods and future trends for physical activity
Background: Methodological limitations make comparison of various instruments difficult, although the number of publications on physical activity assessment has extensively increased. Therefore, systematization of techniques and definitions is essential for the improvement of knowledge in the area. Objective: This paper systematically describes and compares up-to-date methods that assess habitual physical activity and discusses main issues regarding the use and interpretation of data collected with these techniques. Methods: A general outline of the measures and techniques described above is presented in review form, along with their respective definition, usual applications, positive aspects and shortcomings. Results and Conclusions: The various factors to be considered in the selection of physical activity assessment methods include goals, sample size, budget, cultural and social/environmental factors, physical burden for the subject, and statistical factors, such as accuracy and precision. It is concluded that no single current technique is able to quantify all aspects of physical activity under free-living conditions, requiring the use of complementary methods. In not too distant future, devices will take advantage of consumer technologies, such as mobile phones, GPS devices. It is important to perform other activities, such as detecting and responding to physical activity in a real time, creating new opportunities in measurement, remote compliance monitoring, data-driven discovery and intervention.
Assessing physical activity intensity by video analysis
Physiological Measurement, 2015
Assessing physical activity (PA) is a challenging task and many different approaches have been proposed. Direct observation (DO) techniques can objectively code both the behavior and the context in which it occurred, however, they have significant limitations such as the cost and burden associated with collecting and processing data. Therefore, this study evaluated the utility of an automated video analysis system (CAM) designed to record and discriminate the intensity of PA using a subject tracking methodology. The relative utility of the CAM system and DO were compared with criterion data from an objective accelerometry-based device (Actigraph GT3X+). Eight 10 year old children (three girls and five boys) wore the GT3X+ during a standard basketball session. PA was analyzed by two observers using the SOPLAY instrument and by the CAM system. The GT3X+ and the CAM were both set up to collect data at 30 Hz while the DO was performed every two minutes, with 10 s of observation for each gender. The GT3X+ was processed using cut points by Evanson and the outcome measure was the percentage of time spent in different intensities of PA. The CAM data were processed similarly using the same speed thresholds as were used in establishing the Evenson cutoff points (light: <2 mph; walking: 2-4 mph; very active: >4 mph). Similar outcomes were computed from the SOPLAY default analyses. A chi-square test was used to test differences in the percentage of time at the three intensity zones (light, walking and very active). The Yates' correction was used to prevent overestimation of statistical significance for small data. When compared with GT3X+, the CAM had better results than the SOPLAY. The chi-square test