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opencc-by-nc-nd-4.0Jun 2020View details →

ShimFall&ADL: Triaxial accelerometer fall and activities of daily living detection dataset

ShimFall&ADL dataset   Version 1.0 (2020-06-19) Please cite as: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"   Disclaimer While every care has been taken to ensure the accuracy of the data included in the ShimFall&ADL dataset, the authors and the University of the West of Scotland do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom. Contact For inquiries regarding the ShimFall&ADL dataset, please contact: Dr Stamos Katsigiannis, Stamos.Katsigiannis@uws.ac.uk, University of the West of Scotland Prof. Naeem Ramzan, Naeem.Ramzan@uws.ac.uk, University of the West of Scotland   Acknowledgment The authors would like to thank Md. Hasan Shahriar for the data collection under his MSc project.   Dataset summary The ShimFall&ADL dataset contains recordings from 35 individuals, acquired using a chest-strapped Shimmer v2 tri-axial accelerometer, recording at a 50Hz sampling rate. Experiments were conducted in a controlled environment at a research lab in the University of the West of Scotland. Thirty five (35) healthy individuals were recruited among young or mid-aged volunteers, aged between 19 and 34 years old, having a body weight between 52 and 113 kg, and a body height between 1.45 and 1.82 m. Participants performed the following activities of daily living (ADL): Jumping Lying down Bending/picking up Sitting to a chair Standing up from a chair Walking Participants performed the following falls: Steep (hard) Front (soft) Front (hard) Left  (soft) Left  (hard) Right (soft) Right (hard) Back  (soft) Back  (hard) Data Each ".dat" file in the dataset corresponds to one event for one individual and contains 101 accelerometer samples corresponding to the event. Each row of the file corresponds to one 3-channel sample, dividing the x, y, z axes values using the "\t" character, as follows: Row 1: x1\ty1\tz1 Row 2: x2\ty2\tz2 ... Row N: xN\tyN\tzN The files within the dataset are named as follows: adl_<ADL activity>_<Participant ID>.dat <Fall Type>fall_<soft,hard>_<Participant ID>.dat For example, the file "adl_standingfromchair_18.dat" corresponds to the accelerometer recording of the 18th participant, performing the "standing up from chair" ADL. The file, "leftfall_soft_11.dat" corresponds to the accelerometer recording of the 11th participant, performing a soft left fall. Additional information For additional information regarding the creation of the ShimFall&ADL dataset, please refer to the associated publication: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"

opencc-by-4.0Nov 2021View details →

Rest-activity daily rhythm and physical activity levels after hip and knee joint replacement: the role of actigraphy in orthopedic clinical practice.

Elective hip and knee joint replacement for osteoarthritis are cost-effective surgical procedures requiring specific rehabilitation programs. Actigraphy is widely used in both research and clinical practice to study activity patterns with great accuracy and validity but it has never been utilized in orthopedic patients. Therefore, the aim of this study was to objectively assess, through actigraphy, physical activity (PA) levels and rest-activity daily rhythm (RAR) in patients undergoing hip or knee joint replacement and hospitalized for ten days after surgery. Twenty subjects (11 males and 9 females; age: 62.68 ± 10.39 years old; BMI: 29.03 ± 3.92 kg/m2) wore the Actiwatch 2 actigraph (Philips Respironics, Portland, OR) to record both PA levels and RAR for 11 consecutive days and data on subjective scores of pain, by a visual analog scale (VAS), and functional and clinical scores were collected. The following time-points were considered for the statistical analysis: pre-surgery (PRE), the first (POST1), the fourth (POST4) and the tenth (POST10) day after surgery. RAR were processed with the population mean cosinor to describe the rhythm's characteristics (acrophase, amplitude and MESOR) while data on actigraphy-based PA, VAS, and functional clinical scores were compared among PRE, POST1, POST4 and POST 10 with the RM-ANOVA or the non-parametric Friedman test. The day after surgery the subjects had a flattened RAR compared to the other conditions: lower values were detected in POST1 compared to both PRE, POST4 and POST10 for MESOR (p < .0001; η2p = .71, large) and amplitude (p < .0001; η2p = .63, large) while RAR's acrophase (p < .0001; η2p = .61, large) was delayed in PRE (16:45) compared to POST1 (12:42), POST4 (14:38), and POST10 (14:38). PA levels were significantly lower at POST 1 (76.7 ± 33.4) compared to PRE (192.3 ± 91.5; p < .0001 and ES: 1.68, large), POST4 (137.9 ± 45.9; p < .0001 and ES: 1.54, large), and POST10 (131.2 ± 54.3; p < .0001 and ES: 1.21, large) whereas VAS and functional clinical values significantly improved at POST10. Hip and knee joint replacement negatively influenced RAR and PA the first day after surgery but a progressive improvement in the circadian pattern of rest-activity cycle, PA levels, VAS and functional ability was recorded from POST4 to POST10. Actigraphy has the ability to collect real-life data without interfering with clinical practice and give clinicians a new measure of performance that is currently not available. This tool could allow to identify patients with disrupted circadian rhythm and reduced PA in the peri-operative period in orthopedic surgery, and timely intervene on these subjects with personalized rehabilitative intervention.

dryad32/100

Microhabitat use, daily activity pattern and diet of Liolaemus etheridgei in the Andean Polylepis forests of Arequipa, Peru

This study describes the microhabitat use, daily activity pattern and diet of Liolaemus etheridgei Laurent 1998 in the El Simbral and Tuctumpaya Polylepis forests in Arequipa, Peru. El Simbral is a fragmented forest, whereas Tuctumpaya is unfragmented. Our results reveal that L. etheridgei shows no positive selection for any of the microhabitats we identified in Polylepis forests; on the contrary, it selects negatively against Polylepis trees and non-thorny bushes. The daily activity patterns indicate a bimodal pattern with peaks at 9:00-10:59 and 13:00-13:59 h. The diet of L. etheridgei consists mainly of plant material, and the most important animal prey category is Lygaeidae: Hemiptera, which is selected for positively. In particular, microhabitat selection varied for non-thorny bushes, which were selected negatively in the Tuctumpaya population but neither positively nor negatively in the El Simbral population. According to the proportions of plant material found, the L. etheridgei from El Simbral were found to be omnivorous, whereas the Tuctumpaya population washerbivorous. However, the percentage of plant material consumed in the El Simbral population was close to the critical value for herbivory-omnivory. We conclude that the three ecological aspects of L. etheridgei studied here are virtually identical in El Simbral and Tuctumpaya; therefore, this species is not affected significantly by the current fragmentation of forest.

opencc-zeroSep 2022View details →

opencc-by-4.0Sep 2022View details →

watchHAR: A Smartwatch IMU dataset for Activities of Daily Living

<pre># watchHAR: A Smartwatch IMU dataset for Activities of Daily Living This is a dataset of IMU recordings (3-axial acceleration, 3-axial gyroscope, and 3-axial magnetometer) recorded with Sony Smartwatch 3, along with ground truth location data from a vicon motion capture system. Smartwatch IMU and vicon location data exist for the users' left and right hands, as well as vicon location data from their left and right ankles. The recoded data cover the following `activity_id`s: * `brushing_teeth` * `idle` * `preparing_sandwich` * `reading_book` * `typing` * `using_phone` * `using_remote_control` * `walking_freely` * `walking_holding_a_tray` * `walking_with_handbag` * `walking_with_hands_in_pockets` * `walking_with_object_underarm` * `washing_face_and_hands` * `washing_mug` * `washing_plate` * `writing`, all of which were recorded in the same room, across the span of several days. In addition to these activities, walking stairs up and down (`activity_id`s `stairs_up` and `stairs_down`) events were recorded in various different locations. Note that for these stairs events only Smartwatch IMU recordings exist, and only from the dominant hand of the participants, which coincided with the right hand in all cases. The data are organised in the following folder structure: `user_id/activity_id/recording_type.csv`. Stair events are a special case in that multiple up/down stair recordings can exist for a single user. These are separated like the following example ``` `user_01/stairs_down-02/smartwatch_right_hand.csv` ``` that points to the IMU data of the second `stairs_down` recording of the first user. Finally, note that all timestamps refer to a UTC timezone, and that data collection took place in the United Kingdom. </pre>

Figure 2 in Climatic dependence in the daily and seasonal calling activity of anurans from coastal wetlands of southernmost Brazil

Figure 2. Rose-diagram of the circular analysis and mean vector length (r), indicated by the arrow vector, of the number of anuran species exhibiting calling activity (a; r = 0.36) and frequency of the calling activity records throughout the day (b; r = 0.46). The arrow vector indicates the concentration of species exhibiting calling activities throughout the months (a) and the recording frequency of these species throughout the hours of the day (b).

Figure 1 in Climatic dependence in the daily and seasonal calling activity of anurans from coastal wetlands of southernmost Brazil

Figure 1. Map of the study area. (a) The North and South America continent. (b) The Rio Grande do Sul state, Brazil. (c) The area of the TAIM Ecological Station.

Figure 3 in Climatic dependence in the daily and seasonal calling activity of anurans from coastal wetlands of southernmost Brazil

Figure 3. (a) Monthly variation of the maximum (continuous line) and minimum (dashed line) water temperature (°C), minimum absolute air relative humidity (dotted line; %), and hours of rainfall from December 2012 to April 2014. (b) Monthly calling activity of the anuran amphibians community in a wetland area from southernmost Brazil from December 2012 to April 2014. The number of species that exhibited calling activities in each month is shown at the top of the graph and the following categories were used: white – no records; dotted area – between 0.1 and 3.99 hours of daily activity; horizontal bar – between 4 and 7.99 hours of daily activity; dark grey – between 8 and 11.99 hours of daily activity; black – between 12 and 17 hours of daily activity; and plus symbol – activity peak indicated by the mean vector (µ) of the circular analysis.

Figure 4 in Climatic dependence in the daily and seasonal calling activity of anurans from coastal wetlands of southernmost Brazil

Figure 4. Calling activity frequency of the species of anuran amphibians found in wetlands from southernmost Brazil, highlighting the seasons with greatest activity: spring 2013 (September– November 2013); summer 2013 (December 2012–February 2013) and summer 2014 (December 2013–February 2014). The number of records per hour is represented by: white – no records; dotted area – 1-7 records; horizontal bar – 8-14 records; dark grey – 15-21 records; black – 22- 36 records; and plus symbol – activity peak indicated by the mean vector (µ) of the circular analysis.

opennotspecifiedApr 2000View details →

FIG. 2 in Daily activity schedule, gregariousness, and defensive behaviour in the Neotropical harvestman Goniosoma longipes (Opiliones: Gonyleptidae)

FIG. 2. Small aggregation of Goniosoma longipes on a cave wall at Parque Florestal do Itapetinga, South-east Brazil. Note the overlapping of legs. Scale bar = 2 cm.

opennotspecifiedApr 2000View details →

FIG. 1 in Daily activity schedule, gregariousness, and defensive behaviour in the Neotropical harvestman Goniosoma longipes (Opiliones: Gonyleptidae)

FIG. 1. Activity schedule of Goniosoma longipes at Parque Florestal do Itapetinga, Southeast Brazil. The moon and the sun indicate dusk and dawn, respectively.

opencc-by-4.0Aug 2023View details →

Multi-modal Dataset of Human Activities of Daily Living with Ambient Audio, Vibration and Environmental Data

<pre>This dataset provides over 43000 samples of 25 different human activities (e.g. walking, opening/closing a door, sitting down, vacuum cleaning). Each sample is recorded by 5 sensor devices with multiple types of sensors. The main part is audio and vibration. Further metrics were recorded at a low frequency, and are: infrared array, light color, temperature, relative humidity, atmospheric pressure, air quality measure, volatile organic compounds and CO2 equivalent. The data was recorded in supervised sessions to label each sample. The recording environment consisted of a kitchen and dining room. Flawed samples were removed and the different metrics were synchronized, but no further processing or filtering of the data was performed. </pre>

Evaluation of a Computerized Complex Instrumental Activities of Daily Living Marker (NMI)

ClinicalTrials.gov study NCT02843529. IPD Sharing: UNDECIDED. Countries: 0. Publications: 18.

Using Step Count to Enhance Daily Physical Activity in Pulmonary Hypertension

ClinicalTrials.gov study NCT03043976. IPD Sharing: Not stated. Countries: 1. Publications: 9.

Effects of Respiratory Muscle Training and Respiratory Exercise in Exercise Tolerance, Performing Daily Life Activities and Quality of Life of Patients With Chronic Obstructive Pulmonary Disease

ClinicalTrials.gov study NCT01510041. IPD Sharing: Not stated. Countries: 1. Publications: 8.

Daily Activity After Corticosteroids Injection Among Knee Osteoarthritis Patients

ClinicalTrials.gov study NCT02049879. IPD Sharing: Not stated. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Program Evaluation of an In-school Daily Physical Activity Initiative

ClinicalTrials.gov study NCT03618927. IPD Sharing: NO. Countries: 1. Publications: 10.

closedIPD-NOFeb 2026View details →

Kinesiotherapy on Upper Limb Function and Activities of Daily Living in Children With Hemiplegic Cerebral Palsy

ClinicalTrials.gov study NCT07244081. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Strategy for Daily Activity Improvement in Patients With Parkinson's Disease

ClinicalTrials.gov study NCT03972241. IPD Sharing: NO. Countries: 1. Publications: 5.

The Impact of Light Conditions on the Efficacy of Multifocal Intraocular Lens Implantation in Activities of Daily Living

ClinicalTrials.gov study NCT05359380. IPD Sharing: Not stated. Countries: 1. Publications: 3.