BACKGROUNDFew studies have examined the non-linear relationships of objectively-measured sedentary behavior and physical activity with insomnia symptoms in older adults. We investigated such ...relationships of sedentary and physically-active behaviors with total sleep time and nocturnal wakefulness. METHODSWe recruited adults aged 60 years and above who have received health check-ups or been to geriatric outpatient services from a hospital setting. Sedentary and physically-active behaviors, total sleep time, and wakefulness time after sleep onset were measured by Actigraphy, and their relationships were estimated using generalized additive models. RESULTSThe 157 older adults receiving health-related services slept 7.5 h (20.8 min awake) on average per day. Total sleep time was negatively associated with sedentary and physically-active behaviors. By contrast, a U-shape relationship was found between sedentary behavior and wakefulness time after sleep onset, with a turning point at a daily sedentary time of 10.9 h. CONCLUSIONLonger high-intensity physical activity time was related to a shorter wakefulness time after sleep onset. By contrast, daily sedentary time longer than 10.9 h was related to shorter total sleep time but more nocturnal wakefulness time. Future nonpharmacological strategies for sleep improvement should consider the sedentary threshold.
•The structure combines the advantages of cantilever beam and circular diaphragm.•The structure adopts single sensing element and single inertial mass block.•The sensitivity and lateral ...anti-interference ability are improved.
To meet the requirements for low-frequency vibration monitoring, a new type of FBG (fiber Bragg grating) accelerometer based on diaphragm-type cantilever is proposed. The theory analysis of the structure was carried out and the finite element model was constructed to simulate and analyze the acceleration sensing characteristic of the sensor. Simultaneously, the tested results of sensing characteristic from the shaking table indicate that the system has excellent response to low-frequency acceleration excitation signal when the natural frequency of the system is 90 Hz. The frequency response range of the system is 5.0–60.0 Hz, in which the acceleration sensitivity is 485.75 pm/g. The acceleration sensor is designed with strong lateral immunity since the sensitivity in the transverse sensitivity is only 3.6% of the sensitivity in the working direction.
High-G MEMS accelerometer (HGMA) has applications in aerospace, explosive and penetrative environments, and so on. Reliable encapsulation is the basis of ensuring the survival and function of the ...sensor in harsh environments, but complete high-g accelerometer sensor encapsulation models are rarely reported. We use a multiple-degree-of-freedom system and the theory of stress wave interface transfer to design and analyze three different packaging methods for high-g accelerometer sensors. We also determine the best encapsulation method by establishing natural frequency calculation and stress wave transfer models between media. The stress wave reflection ability of patch adhesive is verified with a split Hopkinson pressure bar (SHPB). The results indicate that the unloading efficiency of patch adhesive to the stress wave is up to 95%. The HGMA frequency response is greater than 300 kHz, which was measured by a single Hopkinson rod. The anti-impact performance and the measurement accuracy in the ultra-high overload environment of the high-g acceleration sensor were verified by artillery penetration tests. The results show that the sensor can realize high precision measurements under ultra-high overload environments (the measuring accuracy of the sensor under the impact of 200,000 g is better than 5%).
To evaluate the physical activity in free-living environment, we can used accelerometer. For children with cerebral palsy, we have tried to determine the best algorythm to evaluate the level of ...physical activity.
OBJECTIVES:The aim was to investigate whether patients who participated in a mobility program in the ICU performed better on functional status, muscle, mobility, and respiratory assessments upon ...discharge than patients who received conventional physiotherapy.
DESIGN:Randomized controlled trial.
SETTING:Blind evaluation.
PATIENTS:Adults with previous functional independence and without contraindications for mobilization were eligible.
INTERVENTIONS:The intervention group participated in an early and progressive mobility program with five levels of activity. The control group underwent the conventional treatment without a preestablished routine. We evaluated functional status, level of activity, respiratory status, muscle strength, and mobility at ICU discharge.
MEASUREMENTS AND MAIN RESULTS:We analyzed 49 patients in the control group and 50 patients in the intervention group. Our data showed patients with better functional status and more functionally independent patients in the intervention group compared with those in the control group (96% vs 44%; p < 0.001). The results of the sit-to-stand and 2-minute walk tests, as well as the results of the maximum voluntary ventilation tests, also varied between the groups. The intervention group had shorter ICU stays than the control group. Higher Barthel index scores were associated with the amount of activity and participation in the protocol. The benefits to functional status remained during follow-up.
CONCLUSIONS:Patients who participated in an ICU mobility program had better functional status at discharge from the ICU. The other benefits of the program included better performance in the mobility tests and improved maximum voluntary ventilation performance.
Accelerometers are commonly used in clinical and epidemiological research for more detailed measures of physical activity and to target the limitations of self‐report methods. Sensors are attached at ...the hip, wrist and thigh, and the acceleration data are processed and calibrated in different ways to determine activity intensity, body position and/or activity type. Simple linear modelling can be used to assess activity intensity from hip and thigh data, whilst more advanced machine‐learning modelling is to prefer for the wrist. The thigh position is most optimal to assess body position and activity type using machine‐learning modelling. Frequency filtering and measurement resolution needs to be considered for correct assessment of activity intensity. Simple physical activity measures and statistical methods are mostly used to investigate relationship with health, but do not take advantage of all information provided by accelerometers and do not consider all components of the physical activity behaviour and their interrelationships. More advanced statistical methods are suggested that analyse patterns of multiple measures of physical activity to demonstrate stronger and more specific relationships with health. However, evaluations of accelerometer methods show considerable measurement errors, especially at individual level, which interferes with their use in clinical research and practice. Therefore, better objective methods are needed with improved data processing and calibration techniques, exploring both simple linear and machine‐learning alternatives. Development and implementation of accelerometer methods into clinical research and practice requires interdisciplinary collaboration to cover all aspects contributing to useful and accurate measures of physical activity behaviours related to health.
Physical activity measurement requires good methodological knowledge to achieve measures of high precision and accuracy. Interdisciplinary collaboration facilitates implementation of physical activity into clinical practice as a vital sign of equal importance as any other clinical measure.
For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been ...extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.
Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing ...this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished.
In this article, a linear triaxial magnetometer calibration model with six observations is proposed for wearable inertial measurement unit (IMU) sensors. First, the information matrix of the ...six-parameter calibration model is derived under the proposed six-observation scheme, and its G-optimality is proven. Thereafter, a series of simulation studies were conducted to demonstrate the effectiveness and robustness of the six-observation scheme. Finally, the designed six-observation scheme is experimentally verified by the commercial IMU devices. The simulation and experimental results both demonstrate the efficiency and accuracy of the proposed optimal six-observation scheme. This scheme simplifies the calibration procedure and reduces the calculation workload, further providing guidance for the daily use of wearable sensor calibration.
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