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121 results for “activities of daily living”
A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living
<p>KIN-MUS UJI Dataset contains 572 recordings with anatomical angles and forearm muscle activity of 22 subjects while performing 26 representative activities of daily living. This dataset is, to our knowledge, the biggest currently available hand kinematics and muscle activity dataset to focus on goal-oriented actions. Data were recorded using a CyberGlove instrumented glove and surface EMG electrodes, both properly synchronised. Eighteen hand anatomical angles were obtained from the glove sensors by a validated calibration procedure. Surface EMG activity was recorded from seven representative forearm areas. The statistics verified that data were not affected by the experimental procedures and were similar to the data acquired under real-life conditions.</p> <p> </p> <p><strong>Data Sets</strong>:</p> <p>Data are presented as a Matlab data structure (<em>.mat</em> file). This structure contains all the recorded kinematic and muscle activity data classified as: ADL, phase (reaching, manipulation or release) and subject. The fields contained in the structure are those detailed in the following scheme:</p> <ul> <li>Subject: subject ID;</li> <li>ADL: ADL ID;</li> <li>Phase: phase of movement. 1 corresponds to reaching; 2 corresponds to manipulation; 3 corresponds to releasing.</li> <li>Time: Time stamp</li> <li>Angles (18 columns): Calibrated anatomical angles</li> <li>Muscle activity (7 columns): Normalised signal for the seven representative spot areas, according to [1].</li> </ul> <p>RAW_EMG struct provides the raw sEMG data, without any filter and not resampled, so that researchers may choose to condition the signals as they please<strong>.</strong> The fields contained in this structure are those detailed in the following scheme:</p> <ul> <li>Subject: subject ID;</li> <li>ADL: ADL ID, according to Table 1;</li> <li>Time: Time stamp; this field corresponds with the time stamp of the previous structure.</li> <li>Raw EMG data (7 columns): Raw sEMG data without any filter and not resampled, for the seven representative spot areas according to [1].</li> </ul> <p>[1] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. <em>J. NeuroEngineering Rehabil. </em>(2018).</p>
Fine-Grained Activities of Daily Living Data with Structural Vibration and Electrical Load Sensing
<p>Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. We utilize structural vibration sensing and electrical load sensing to acquire multiple fine-grained kitchen activities under a lab structure setting.</p> <p>Each file contains the following values:<br> -RawData: time series of data for each channel (vibration on the table, vibration on the floor, load)<br> -Label: manually fine-grained labels of events<br> -Table: detected events start/stop index for vibration sensor on the table<br> -Floor: detected events start/stop index for vibration sensor on the floor<br> -Load: detected events start/stop index for load sensor<br> <br> Label notation:<br> 1 -- operating the kettle<br> 2 -- kettle on<br> 3 -- operating the microwave<br> 4 -- microwave on<br> 5 -- put things on the stove<br> 6 -- operating with stove<br> 7 -- stove on<br> 8 -- operating vacuum<br> 9 -- sweep floor<br> 10 -- walking/step<br> 11 -- miscellaneous<br> 12 -- synchronization signal (knock on the floor)<br> 13 -- vacant<br> 14 -- microwave door open</p>
MOVMUS-UJI Dataset & ERGOMOVMUS: EMG and kinematics data of the hand in activities of daily living with special interest for ergonomics
<p>A dataset of <strong>human hand kinematics</strong> and <strong>forearm muscle activation</strong> collected during the performance of a wide variety of activities of daily living (ADLs) is presented, with tagged characteristics of products and tasks. A total of <strong>26 participants</strong> performed <strong>161 ADLs</strong>, selected to be representative of common elementary tasks, grasp types, product orientations and performance heights. 105 products were used, being varied regarding shape, dimensions, weight and type (common products and assistive devices).</p> <p>The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each and seven surface EMG sensors per arm recording muscle activity. The products and their arrangement were the same across subjects, and tasks were performed in a guided way. Data of <strong>more than 4100 ADLs</strong> is presented in this dataset as <strong>Matlab structures</strong> with full continuous recordings, which may be used in applications such as machine learning or to characterize healthy human hand behaviour.</p> <p>The dataset is accompanied with a <strong>custom data visualization application (ERGOMOVMUS)</strong> as a tool for ergonomics applications, allowing visualization and calculation of aggregated data from specific task, product and/or subjects’ characteristics.</p> <p> </p> <p><strong>v3.0 includes the following updates:</strong></p> <p>- Statistical summary of the recordings both in .xlsx and .ods file format (v1.1 only included it in .xlsx file format)</p> <p>- Updated experiment details in "MOVMUS-UJI DATASET GUIDE.pdf".</p>
Transhumeral Loading During Advanced Upper Extremity Activities of Daily Living
<p>Percutaneous osseointegrated (OI) implants for direct skeletal attachment of upper extremity prosthetics represent an alternative to traditional socket suspension that may yield improved patient function and satisfaction. This is especially true in high-level, transhumeral amputees where prosthetic fitting is challenging and abandonment rates remain high. However, maintaining mechanical integrity of the bone-implant interface is crucial for safe clinical introduction of this technology. The collection of population data on the transhumeral loading environment will aid in the design of compliance and overload protection devices that mitigate the risk of periprosthetic fracture. We collected marker-based upper extremity kinematic data from non-amputee volunteers during advanced activities of daily living (AADLs) that applied dynamic loading to the humerus. These kinematic data are available for download and will aid in the development of overload protection devices and appropriate post-operative rehabilitation protocols that balance return to an active lifestyle with patient safety.</p>
Data from: Immune challenge reduces daily activity period in free-living birds for three weeks
<p>Non-lethal infections are common in free-living animals and the associated sickness behaviours can impact crucial life-history trade-offs. However, little is known about the duration and extent of such sickness behaviours in free-living animals, and consequently how this affects life-history decisions. Here, free-living Eurasian blackbirds <em>Turdus merula</em> were immune-challenged with lipopolysaccharide (LPS) to mimic a bacterial infection, and their behaviour was monitored for up to 48 days using accelerometers. As expected, immune-challenged birds were less active than controls within the first 24 hours. Unexpectedly, this reduced activity remained detectable for 20 days, before both groups returned to similar activity levels. Furthermore, activity was positively correlated with a pre-experimental index of complement activity, but only in immune-challenged birds, suggesting that sickness behaviors are modulated by constitutive immune function. Differences in daily activity levels stemmed from immune-challenged birds resting earlier at dusk than control birds, while activity levels between groups were similar during core daytime hours. Overall, activity was reduced by 19% in immune-challenged birds and they were on average almost 1 hour less active per day for 20 days. This unexpected longevity in sickness behaviour may have severe implications during energy-intense annual-cycle stages (e.g., breeding, migration, winter). Thus, our data help to understand the consequences of non-lethal infections on free-living animals.</p>
A five-sensor IMU-based Parkinson's disease patient and control dataset including three activities of daily living
<p class="MsoNormal">Parkinson's disease is an often-debilitating progressive neurological condition leading to loss of motor control. This dataset contains kinematic sensor data from two groups: one containing 15 patients with Parkinson's disease, and a control group of 19 participants without any known neurological condition. Participant ages ranged from 40–85, 21 were male, and 13 were female. The participants wore five 9-axis Inertial Measurement Units (IMUs) – one on each upper arm, each lower arm, and on their head. They were asked to perform a calibration pose, followed by three activities: making toast, putting on a cardigan, and unlocking and opening a door, with each activity repeated three times. The IMUs recorded time-series acceleration and orientation data from the moment where the participant was instructed to begin the activity (inception of the idea to act), through to the activity's completion. This dataset is planned for use in intent-sensing studies for assistive device control but is also applicable for activity recognition.</p>
Open-Label Study With Pimavanserin on Activities of Daily Living in Subjects With Parkinson's Disease Psychosis
ClinicalTrials.gov study NCT04292223. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation of the Effects of Aquatic Therapy on Activities of Daily Living, Walking, Balance, Posture, Pain, and Depression in Parkinson's Patients
ClinicalTrials.gov study NCT07390825. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Rivastigmine Patch on Activities of Daily Living and Cognition in Patients With Severe Dementia of the Alzheimer's Type (ACTION) (Study Protocol CENA713DUS44, NCT00948766) and a 24 Week Ope
ClinicalTrials.gov study NCT00948766. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Evaluation of Outcomes for Quality of Life and Activities of Daily Living for BKP in the Treatment of VCFs
ClinicalTrials.gov study NCT01871519. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Assessing a Novel Virtual Environment That Assists With Activities of Daily Living
ClinicalTrials.gov study NCT05418296. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Immune challenge reduces daily activity period in free-living birds for three weeks
Open the record for dataset details and reuse information.
A five-sensor IMU-based Parkinson's disease patient and control dataset including three activities of daily living
Open the record for dataset details and reuse information.
ShimFall&ADL: Triaxial accelerometer fall and activities of daily living detection dataset
<p> </p> <p><strong>ShimFall&ADL dataset</strong></p> <p> </p> <p><strong>Version </strong>1.0 (2020-06-19)</p> <p><strong>Please cite as:</strong> "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"</p> <p> </p> <p><strong>Disclaimer</strong><br> 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.</p> <p><br> <strong>Contact</strong><br> For inquiries regarding the ShimFall&ADL dataset, please contact:<br> Dr Stamos Katsigiannis, Stamos.Katsigiannis@uws.ac.uk, University of the West of Scotland<br> Prof. Naeem Ramzan, Naeem.Ramzan@uws.ac.uk, University of the West of Scotland</p> <p> </p> <p><strong>Acknowledgment</strong></p> <p>The authors would like to thank Md. Hasan Shahriar for the data collection under his MSc project.</p> <p> </p> <p><strong>Dataset summary</strong><br> 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.</p> <p>Participants performed the following activities of daily living (ADL):<br> Jumping<br> Lying down<br> Bending/picking up<br> Sitting to a chair<br> Standing up from a chair<br> Walking</p> <p>Participants performed the following falls:<br> Steep (hard)<br> Front (soft)<br> Front (hard)<br> Left (soft)<br> Left (hard)<br> Right (soft)<br> Right (hard)<br> Back (soft)<br> Back (hard)</p> <p><br> <strong>Data</strong><br> 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:<br> Row 1: x1\ty1\tz1<br> Row 2: x2\ty2\tz2<br> ...<br> Row N: xN\tyN\tzN</p> <p>The files within the dataset are named as follows:<br> adl_<ADL activity>_<Participant ID>.dat<br> <Fall Type>fall_<soft,hard>_<Participant ID>.dat</p> <p>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.</p> <p><br> <strong>Additional information</strong><br> 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"</p>
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>
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.
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.
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.
Cognitive Stimulation in Daily Activities for People Living With Early to Middle Stage Dementia
ClinicalTrials.gov study NCT06147479. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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