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Activities of Daily Living Object DatasetOverviewThe ADL (Activities of Daily Living) Object Dataset is a curated collection of images and annotations specifically focusing on objects commonly interacted with during daily living activities. This dataset is designed to facilitate research and development in assistive robotics in home environments.Data Sources and LicensingThe dataset comprises images and annotations sourced from four publicly available datasets:COCO DatasetLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation:Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common Objects in Context. European Conference on Computer Vision (ECCV), 740–755.Open Images DatasetLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation:Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Duerig, T., & Ferrari, V. (2020). The Open Images Dataset V6: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale. International Journal of Computer Vision, 128(7), 1956–1981.LVIS DatasetLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation:Gupta, A., Dollar, P., & Girshick, R. (2019). LVIS: A Dataset for Large Vocabulary Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5356–5364.Roboflow UniverseLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation: The following repositories from Roboflow Universe were used in compiling this dataset:Work, U. AI Based Automatic Stationery Billing System Data Dataset. 2022. Accessible at: https://universe.roboflow.com/university-work/ai-based-automatic-stationery-billing-system-data (accessed on 11 October 2024).Destruction, P.M. Pencilcase Dataset. 2023. Accessible at: https://universe.roboflow.com/project-mental-destruction/pencilcase-se7nb (accessed on 11 October 2024).Destruction, P.M. Final Project Dataset. 2023. Accessible at: https://universe.roboflow.com/project-mental-destruction/final-project-wsuvj (accessed on 11 October 2024).Personal. CSST106 Dataset. 2024. Accessible at: https://universe.roboflow.com/personal-pgkq6/csst106 (accessed on 11 October 2024).New-Workspace-kubz3. Pencilcase Dataset. 2022. Accessible at: https://universe.roboflow.com/new-workspace-kubz3/pencilcase-s9ag9 (accessed on 11 October 2024).Finespiralnotebook. Spiral Notebook Dataset. 2024. Accessible at: https://universe.roboflow.com/finespiralnotebook/spiral_notebook (accessed on 11 October 2024).Dairymilk. Classmate Dataset. 2024. Accessible at: https://universe.roboflow.com/dairymilk/classmate (accessed on 11 October 2024).Dziubatyi, M. Domace Zadanie Notebook Dataset. 2023. Accessible at: https://universe.roboflow.com/maksym-dziubatyi/domace-zadanie-notebook (accessed on 11 October 2024).One. Stationery Dataset. 2024. Accessible at: https://universe.roboflow.com/one-vrmjr/stationery-mxtt2 (accessed on 11 October 2024).jk001226. Liplip Dataset. 2024. Accessible at: https://universe.roboflow.com/jk001226/liplip (accessed on 11 October 2024).jk001226. Lip Dataset. 2024. Accessible at: https://universe.roboflow.com/jk001226/lip-uteep (accessed on 11 October 2024).Upwork5. Socks3 Dataset. 2022. Accessible at: https://universe.roboflow.com/upwork5/socks3 (accessed on 11 October 2024).Book. DeskTableLamps Material Dataset. 2024. Accessible at: https://universe.roboflow.com/book-mxasl/desktablelamps-material-rjbgd (accessed on 11 October 2024).Gary. Medicine Jar Dataset. 2024. Accessible at: https://universe.roboflow.com/gary-ofgwc/medicine-jar (accessed on 11 October 2024).TEST. Kolmarbnh Dataset. 2023. Accessible at: https://universe.roboflow.com/test-wj4qi/kolmarbnh (accessed on 11 October 2024).Tube. Tube Dataset. 2024. Accessible at: https://universe.roboflow.com/tube-nv2vt/tube-9ah9t (accessed on 11 October 2024). Staj. Canned Goods Dataset. 2024. Accessible at: https://universe.roboflow.com/staj-2ipmz/canned-goods-isxbi (accessed on 11 October 2024).Hussam, M. Wallet Dataset. 2024. Accessible at: https://universe.roboflow.com/mohamed-hussam-cq81o/wallet-sn9n2 (accessed on 14 October 2024).Training, K. Perfume Dataset. 2022. Accessible at: https://universe.roboflow.com/kdigital-training/perfume (accessed on 14 October 2024).Keyboards. Shoe-Walking Dataset. 2024. Accessible at: https://universe.roboflow.com/keyboards-tjtri/shoe-walking (accessed on 14 October 2024).MOMO. Toilet Paper Dataset. 2024. Accessible at: https://universe.roboflow.com/momo-nutwk/toilet-paper-wehrw (accessed on 14 October 2024).Project-zlrja. Toilet Paper Detection Dataset. 2024. Accessible at: https://universe.roboflow.com/project-zlrja/toilet-paper-detection (accessed on 14 October 2024).Govorkov, Y. Highlighter Detection Dataset. 2023. Accessible at: https://universe.roboflow.com/yuriy-govorkov-j9qrv/highlighter_detection (accessed on 14 October 2024).Stock. Plum Dataset. 2024. Accessible at: https://universe.roboflow.com/stock-qxdzf/plum-kdznw (accessed on 14 October 2024).Ibnu. Avocado Dataset. 2024. Accessible at: https://universe.roboflow.com/ibnu-h3cda/avocado-g9fsl (accessed on 14 October 2024).Molina, N. Detection Avocado Dataset. 2024. Accessible at: https://universe.roboflow.com/norberto-molina-zakki/detection-avocado (accessed on 14 October 2024).in Lab, V.F. Peach Dataset. 2023. Accessible at: https://universe.roboflow.com/vietnam-fruit-in-lab/peach-ejdry (accessed on 14 October 2024).Group, K. Tomato Detection 4 Dataset. 2023. Accessible at: https://universe.roboflow.com/kkabs-group-dkcni/tomato-detection-4 (accessed on 14 October 2024).Detection, M. Tomato Checker Dataset. 2024. Accessible at: https://universe.roboflow.com/money-detection-xez0r/tomato-checker (accessed on 14 October 2024).University, A.S. Smart Cam V1 Dataset. 2023. Accessible at: https://universe.roboflow.com/ain-shams-university-byja6/smart_cam_v1 (accessed on 14 October 2024).EMAD, S. Keysdetection Dataset. 2023. Accessible at: https://universe.roboflow.com/shehab-emad-n2q9i/keysdetection (accessed on 14 October 2024).Roads. Chips Dataset. 2024. Accessible at: https://universe.roboflow.com/roads-rvmaq/chips-a0us5 (accessed on 14 October 2024).workspace bgkzo, N. Object Dataset. 2021. Accessible at: https://universe.roboflow.com/new-workspace-bgkzo/object-eidim (accessed on 14 October 2024).Watch, W. Wrist Watch Dataset. 2024. Accessible at: https://universe.roboflow.com/wrist-watch/wrist-watch-0l25c (accessed on 14 October 2024).WYZUP. Milk Dataset. 2024. Accessible at: https://universe.roboflow.com/wyzup/milk-onbxt (accessed on 14 October 2024).AussieStuff. Food Dataset. 2024. Accessible at: https://universe.roboflow.com/aussiestuff/food-al9wr (accessed on 14 October 2024).Almukhametov, A. Pencils Color Dataset. 2023. Accessible at: https://universe.roboflow.com/almas-almukhametov-hs5jk/pencils-color (accessed on 14 October 2024).All images and annotations obtained from these datasets are released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits sharing and adaptation of the material in any medium or format, for any purpose, even commercially, provided that appropriate credit is given, a link to the license is provided, and any changes made are indicated.Redistribution Permission:As all images and annotations are under the CC BY 4.0 license, we are legally permitted to redistribute this data within our dataset. We have complied with the license terms by:Providing appropriate attribution to the original creators.Including links to the CC BY 4.0 license.Indicating any changes made to the original material.Dataset StructureThe dataset includes:Images: High-quality images featuring ADL objects suitable for robotic manipulation.Annotations: Bounding boxes and class labels formatted in the YOLO (You Only Look Once) Darknet format.ClassesThe dataset focuses on objects commonly involved in daily living activities. A full list of object classes is provided in the classes.txt file.FormatImages: JPEG format.Annotations: Text files corresponding to each image, containing bounding box coordinates and class labels in YOLO Darknet format.How to Use the DatasetDownload the DatasetUnpack the Datasetunzip ADL_Object_Dataset.zipHow to Cite This DatasetIf you use this dataset in your research, please cite our paper:@article{shahria2024activities, title={Activities of Daily Living Object Dataset: Advancing Assistive Robotic Manipulation with a Tailored Dataset}, author={Shahria, Md Tanzil and Rahman, Mohammad H.}, journal={Sensors}, volume={24}, number={23}, pages={7566}, year={2024}, publisher={MDPI}}LicenseThis dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).License Link: https://creativecommons.org/licenses/by/4.0/By using this dataset, you agree to provide appropriate credit, indicate if changes were made, and not impose additional restrictions beyond those of the original licenses.AcknowledgmentsWe gratefully acknowledge the use of data from the following open-source datasets, which were instrumental in the creation of our specialized ADL object dataset:COCO Dataset: We thank the creators and contributors of the COCO dataset for making their images and annotations publicly available under the CC BY 4.0 license.Open Images Dataset: We express our gratitude to the Open Images team for providing a comprehensive dataset of annotated images under the CC BY 4.0 license.LVIS Dataset: We appreciate the efforts of the LVIS dataset creators for releasing their extensive dataset under the CC BY 4.0 license.Roboflow Universe:

The PAAL ADL Accelerometry dataset (v2.0) has been acquired with a high-quality wearable multisensor device, the Empatica E4. In this dataset, among the signals collected by the sensors embedded in the Empatica E4, only the acceleration has been extracted to monitor the users performing different activities of daily living (ADLs). To promote the real-life acquisition procedure, subjects acted in their natural environment, with no instructions about how and for how long to perform each activity (other than a minimum time). The device was worn on the dominant hand.

The dataset includes 24 different ADLs performed using real objects. Each activity was repeated between 3 and 5 times (on average) by 52 healthy subjects, characterized by a gender balance (26 women and 26 men), and a large age range (between 18 and 77 years, mean = 44.08 years and standard deviation = 17.06 years).

The PAAL ADL Accelerometry dataset (v2.0) is composed of three files:

users.csv: each line contains (participant id, gender, age) of each user performing the ADLs in the dataset. N.B: gender labels are 'man' and 'woman'.

ADLs.csv: each line contains (ADL id, ADL name)

data.zip: folder with 6,072 files of accelerometer data of users performing ADLs. The name of each file indicates the name of the ADL, the user id and the repetition. Each row in the files represents the continuous gravitational force (g) applied to each of the three spacial dimensions (x, y, and z). The scale is limited to [-2g, +2g]. The sampling frequency is 32 Hz, with a resolution of 0.015 g (8 bit). More information about the format here.

Cette couche de données localise le siège d'exploitation des agences de développement local permettant de faire émerger des projets créateurs d’activités économiques et d’emplois.

Les agences de développement local (ADL) mettent en réseau des partenaires locaux issus des secteurs public, privé et associatif afin de faire émerger des projets créateurs d’activités économiques et d’emplois. L’objectif est de valoriser le potentiel d'un territoire dans une stratégie de développement économique à long terme. Elles sont actives en Wallonie dans une seule commune ou plusieurs communes limitrophes. Le territoire d’action d'une ADL doit compter 40.000 habitants maximum.

Selon le Décret du 25 mars 2004, les ADL doivent être agréées pour pouvoir exercer leurs activités. Un premier agrément est octroyé pour une période de trois ans. Un renouvellement d'agrément est, quant à lui, octroyé pour une période de six ans renouvelable.

Les missions des ADL consistent à: - diagnostiquer les atouts et les faiblesses de son territoire; - établir un plan stratégique de développement économique durable; - définir les actions à mener et se donner les moyens de les évaluer; - réunir les acteurs locaux pour mener des actions créatrices d'emploi; - susciter et coordonner les actions partenariales définies dans le plan d’actions; - accueillir les porteurs de projets, les accompagner et les orienter vers les partenaires utiles;

- stimuler des réseaux au service de l'entreprenariat;

             mettre en évidence les ressources et le savoir-faire.

Chaque ADL met en œuvre un plan d'actions pluri-annuel basé sur les spécificités de son territoire.

Seules les structures agréées par la Commission d’agrément et d’accompagnement des ADL peuvent s’annoncer comme telles, utiliser le logo ADL et bénéficier du réseau ADL.

Il faut savoir que, depuis 2015 et pour une période indéterminée, seules les demandes de renouvellement d'agrément peuvent être analysées par le Service Public de Wallonie, plus aucune commune ne pourra obtenir d’agrément pour la constitution d’une nouvelle agence de développement local (ADL).

Actuellement, 49 ADL agréées sont répertoriées en Région wallonne. La couche de données localise, de manière ponctuelle, le siège d'exploitation de l'ADL. Chaque point est caractérisé par le nom de l'ADL, l'adresse de contact, l'adresse mail du responsable et un lien Web éventuel. Certaines ADL concernent plusieurs communes. Dans ce cas, les différentes communes sont reprises dans le nom du siège. Ces informations permettent de localiser et contacter le siège d'exploitation de l'ADL souhaitée.

Activities of daily living (ADLs) monitoring is essenitial in elderly field as it provides daily activities information for caregivers. In human daily life

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_

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"

Background and objectives: CS-ADL (Cognitive Stimulation in Activities of Daily Living) is an occupational therapist-led group cognitive stimulation program for people living with mild-to-moderate dementia. It aims to enhance and maintain participants’ skills in activities of daily living (ADLs). Previous research has identified participants and caregivers perceive CS-ADL to be an acceptable and beneficial intervention; however, research is required to evaluate the effectiveness of intervention. This study investigated the feasibility of conducting a randomised controlled trial comparing the effect of CS-ADL to treatment-as-usual (TAU).Methodology: This study used a pilot, parallel, non-randomised controlled design. Data regarding participants’ ADL functioning, cognition, communication, quality of life (QOL) and neuropsychiatric symptoms (NPS) were collected at baseline and follow-up. Data were analysed using descriptive and inferential statistics.Results: Thirteen participants were allocated to the intervention group and three were allocated to the control group. A significant reduction in caregiver burden was observed in the treatment group at follow-up (p=0.04). A significant correlation between baseline cognitive functioning and changes in ADL functioning at follow-up were also identified in the treatment group (p=0.03). No other significant findings were reported.Conclusions: Preliminary results indicate CS-ADL may reduce caregiver burden and provide greater benefit to ADL outcomes for individuals with less severe cognitive impairment. However, no significant changes in ADL functioning were observed in this study. Further research is necessary to confirm these findings. Additionally, challenges in recruitment suggest modification of the study design is required to enhance feasibility and rigour of future trials.

LLAVIDAL/ADL-X dataset hosted on Hugging Face and contributed by the HF Datasets community

This is a comprehensive dataset of human arm motion during Activities of Daily Living (ADL). The Cartesian locations of the head, torso, and arm segments were recorded using a motion capture system (Vicon) from 12 participants (ages 18-72, 6 male, 6 female) performing 24 unique tasks. These include both standing and sitting tasks, as well as repetitions, selected based on what would be most useful for prosthesis users, resulting in 72 recorded trials per subject. Dataset was collected and analyzed for identification, categorization, and simplification, of movement patterns for upper-limb prosthesis control in [Gloumakov Y, Spiers AJ, Dollar AM, “Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Three-, Four-, and Seven-Degree-of-Freedom Arm Movements,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020] and in [Gloumakov Y, Spiers AJ, Dollar AM, “Dimensionality Reduction and Motion Clustering during Activities of Daily Living: Decoupling Hand Location and Orientation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020].

Local Development Agencies (LDAs) aim to stimulate a dynamic partnership with the local forces of one or more municipalities serving sustainable development. The aim is to exploit the potential of a territory in a long-term economic development strategy. The ADL indicator indicates if there is an active ADL in the territory (‘yes’ or ‘no’).

The ADL may be in the communal territory or in that of one of the partner municipalities where the ADL covers a territory consisting of several municipalities. An ADL may have several units of establishments spread over the (inter)municipal territory covered. The first ADLs were approved on 1/1/2008, when the decree entered into force for a period of 3 years. Other LDAs were subsequently approved or renewed their approval (2011-2013). Since 2014, municipalities submitting a first application for approval can obtain it for a period of three years. ADLs submitting an application for renewal of authorisation may obtain it for a period of six years.

No new ALDs since 2016 because of a moratorium.

Purpose: Experiencing difficulty with activities of daily living (ADLs) and instrumental ADLs (IADLs) and/or the consequences of unmet ADL/IADL-related needs is associated with adverse health-related outcomes. The association of hearing loss (HL) with experiencing the consequences of unmet ADL/IADL-related needs is not well understood. We investigated the associations of HL with experiencing ADL/IADL difficulties and the consequences of unmet ADL/IADL-related needs in older adults.Method: We investigated cross-sectional associations between audiometric HL, the number of ADL and IADL difficulties, and the number of consequences of unmet ADL/IADL-related needs among adults aged 65 years and older in the National Health and Aging Trends Study.Results: In 4,724 older adults, 30.5% (n = 1,736) and 30.9% (n = 1,727) had self-reported difficulty with ADLs and IADLs, respectively. Of the 2,289 participants who reported difficulty with at least one ADL/IADL, 14.0% (n = 741) reported experience of at least one consequence of an unmet ADL/IADL-related need. In multivariable ordinal regression analyses, mild (OR = 1.38, 95% CI [1.1, 1.73]) and moderate or greater (OR = 1.57, 95% CI [1.17, 2.1]) HL were associated with higher odds of difficulties with additional ADLs. Moderate or greater HL was associated with higher odds of reporting difficulties with additional IADLs (OR = 1.59, 95% CI [1.19, 2.12]). There was no significant association between HL and higher odds of having additional consequences of unmet needs.Conclusions: Our results show an association between HL and a higher number of ADL and IADL difficulties. Adults with HL may require increased support to address difficulties with daily activities and prevent experiencing related consequences.Supplemental Material S1. Associations between hearing loss, ADL/IADL difficulty, and experience of unmet ADL/IADL-related needs (hearing loss modeled as continuous variable), National Health and Aging Trends Study, Round 12.Supplemental Material S2. Associations of hearing loss, ADL/IADL difficulty, and experience of unmet ADL/IADL-related needs excluding participants relying on proxy response, National Health and Aging Trends Study, Round 12.Supplemental Material S3. Characteristics of participants with self-reported ADL or IADL difficulty stratified by hearing loss category, National Health and Aging Trends Study, Round 12.Bessen, S., Garcia Morales, E. E., Zhang, W., Martinez-Amezcua, P., Umoh, M., Cudjoe, T. K. M., Schrack, J. A., & Reed, N. S. (2025). Hearing loss, difficulty with activities of daily living, and experience of consequences of related unmet needs in older adults: A cross-sectional analysis. American Journal of Audiology, 34(1), 127–138. https://doi.org/10.1044/2024_AJA-24-00183

This dataset shows the fundamental skills needed for self-care, including basic hygiene, mobility, and eating by SNOMED CT.

Dataset related to the article:

Paneroni M, Scalvini S, Corrà U, Lovagnini M, Maestri R, Mazza A, Raimondo R, Agostoni P, La Rovere MT. The Impact of Cardiac Rehabilitation on Activities of Daily Life in Elderly Patients With Heart Failure. Front Physiol. 2022;12:785501. doi: 10.3389/fphys.2021.785501.

Background: In elderly chronic heart failure (HF) patients, activities of daily living (ADLs) require the use of a high proportion of patients' peak aerobic capacity, heart rate, and ventilation. Objectives: To assess the effects of short-term comprehensive cardiac rehabilitation (CR) on the metabolic requirement of ADLs in elderly patients with chronic HF. Methods: The study population comprised 99 elderly chronic HF patients (mean age 72 ± 5 years, 80% male, 61% ejection fraction <40%, mean NT-proBNP 2,559 ± 4,511 pg/ml) participating in a short-term (mean days 19 ± 7) residential CR program. Before and after CR, participants, while wearing a portable ergospirometer, performed a standardized ADL battery: ADL1 (getting dressed), ADL2 (folding 8 towels), ADL3 (putting away 6 bottles), ADL4 (making a bed), ADL5 (sweeping the floor for 4 min), ADL6 (climbing 1 flight of stairs carrying a 1.5 Kg load), and ADL7 (a standard 6-min walking test). Results: After CR, task-related oxygen uptake did not change in any of the domestic ADLs. Notably, there was a significant decrease in the cumulative time required to perform ADLs (ADL 1-4 and ADL6; from 412 ± 147 to 388 ± 141 s, p = 0.001) and a reduction in maximal heart rate in ADL1 and 3 (p = 0.005 and p = 0.027, respectively). Changes occurred in the 6MWT with an increase in oxygen uptake (p = 0.005) and in the distance covered (p < 0.001) and a significant decrease in the Borg scale of dyspnea (p = 0.004). Conclusion: Elderly patients with chronic heart failure who are engaged in a short-term residential CR program improve the performance of routine ADLs.

This dataset is about books. It has 1 row and is filtered where the author is Sina M. Adl. It features 7 columns including author, publication date, language, and book publisher.

A dataset of human hand kinematics and forearm muscle activation 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 26 participants performed 161 ADLs, 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).

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 more than 4100 ADLs is presented in this dataset as Matlab structures with full continuous recordings, which may be used in applications such as machine learning or to characterize healthy human hand behaviour.

The dataset is accompanied with a custom data visualization application (ERGOMOVMUS) as a tool for ergonomics applications, allowing visualization and calculation of aggregated data from specific task, product and/or subjects’ characteristics.

v3.0 includes the following updates:

- Statistical summary of the recordings both in .xlsx and .ods file format (v1.1 only included it in .xlsx file format)

- Updated experiment details in "MOVMUS-UJI DATASET GUIDE.pdf".

The ADL Piano MIDI is a dataset of 11,086 piano pieces from different genres. This dataset is based on the Lakh MIDI dataset, which is a collection on 45,129 unique MIDI files that have been matched to entries in the Million Song Dataset. Most pieces in the Lakh MIDI dataset have multiple instruments, so for each file the authors of ADL Piano MIDI dataset extracted only the tracks with instruments from the "Piano Family" (MIDI program numbers 1-8). This process generated a total of 9,021 unique piano MIDI files. Theses 9,021 files were then combined with other approximately 2,065 files scraped from publicly-available sources on the internet. All the files in the final collection were de-duped according to their MD5 checksum.

This dataset is about books. It has 2 rows and is filtered where the author is Shirin Adl. It features 2 columns including publication date.

Dataset

This dataset was created by vroneres

Contents

SPHERE house scripted dataset: A multi-sensor dataset with annotated activities of daily living recorded in a residential setting v2.0/data

BackgroundEPs pose significant challenges to individual health and quality of life, attracting attention in public health as a risk factor for diminished quality of life and healthy life expectancy in middle-aged and older adult populations. Therefore, in the context of global aging, meticulous exploration of the factors behind emotional issues becomes paramount. Whether ADL can serve as a potential marker for EPs remains unclear. This study aims to provide new evidence for ADL as an early predictor of EPs through statistical analysis and validation using machine learning algorithms.MethodsData from the 2018 China Health and Retirement Longitudinal Study (CHARLS) national baseline survey, comprising 9,766 samples aged 45 and above, were utilized. ADL was assessed using the BI, while the presence of EPs was evaluated based on the record of “Diagnosed with Emotional Problems by a Doctor” in CHARLS data. Statistical analyses including independent samples t-test, chi-square test, Pearson correlation analysis, and multiple linear regression were conducted using SPSS 25.0. Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.ResultsPopulation demographic analysis revealed a significantly lower average BI score of 65.044 in the “Diagnosed with Emotional Problems by a Doctor” group compared to 85.128 in the “Not diagnosed with Emotional Problems by a Doctor” group. Pearson correlation analysis indicated a significant negative correlation between ADL and EPs (r = −0.165, p