Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control Participants
<p>The dataset includes kinematics data recorded by a Motion Capture System (Vicon Motion System, Oxford, UK) at 100Hz. Data were filtered (fourth-order zero-lag Butterworth low-pass filter with a cutoff frequency of 6 Hz) and exported with Vicon’s Nexus 2.15 software.</p> <p><b> DM2_kinematics_data...
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| Other Authors: | , |
| Format: | dataset |
| Published: |
2025
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| Online Access: | https://doi.org/10.60895/redata/SB0OXW |
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| _version_ | 1868890001548247040 |
|---|---|
| author | Biancardi, Carlo |
| author2 | Bona, Renata Gianneechini, Gonzalo |
| author2_role | author author |
| author_browse | Biancardi, Carlo Bona, Renata Gianneechini, Gonzalo |
| author_facet | Biancardi, Carlo Bona, Renata Gianneechini, Gonzalo |
| author_role | author |
| collection | REDATA |
| dc.contributor.none.fl_str_mv | Biancardi, Carlo |
| dc.creator.none.fl_str_mv | Biancardi, Carlo Bona, Renata Gianneechini, Gonzalo |
| dc.date.none.fl_str_mv | 2025-09-08 |
| dc.identifier.none.fl_str_mv | https://doi.org/10.60895/redata/SB0OXW |
| dc.publisher.none.fl_str_mv | Repositorio de datos abiertos de investigación de Uruguay |
| dc.relation.none.fl_str_mv | Breath-by-breath cardiometabolic data during walking in type 2 diabetes and control participants https://doi.org/10.60895/redata/D2I4UJ |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:REDATA instname:Agencia Nacional de Investigación e Innovación instacron:Agencia Nacional de Investigación e Innovación |
| dc.subject.none.fl_str_mv | Medicine, Health and Life Sciences Type 2 Diabetes Mellitus Clinical gait analysis Motion Capture System |
| dc.title.none.fl_str_mv | Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control Participants |
| dc.type.none.fl_str_mv | Dataset http://purl.org/coar/resource_type/c_ddb1 info:eu-repo/semantics/dataset |
| description | <p>The dataset includes kinematics data recorded by a Motion Capture System (Vicon Motion System, Oxford, UK) at 100Hz. Data were filtered (fourth-order zero-lag Butterworth low-pass filter with a cutoff frequency of 6 Hz) and exported with Vicon’s Nexus 2.15 software.</p> <p><b> DM2_kinematics_data (JSON Dataset)</b></p> <p><b> CG_kinematics_data (JSON Dataset)</b></p> <p>These datasets contain walking trials data collected across 37 subjects: 15 with type 2 diabetes mellitus (DM2) and 22 healthy participants (CG). The groups were paired for age and sex. Each participant performed 5 sessions on a treadmill at different constant speeds: the individual's self-selected walking speed (SSWS), two speeds above and two speeds below the SSWS. During each session, up to three 30-second trials were recorded by the MOCAP system. The data is stored as a hierarchical JSON file for ease of parsing, analysis, and expansion.</p> --- <p><b> Structure</b></p> <p>The data is organized in four hierarchical levels:</p> <p>Subject ID → Speed → Trial Number → Kinematic Variables (markers 3D position)</p> <p>Where:</p> <p>-- **Subject ID**: A numeric string (e.g., `"7"`), - **Speed**: Integer string representing the gait speedin hm/h (e.g., `"18"` means 1.8 km/h). Speed values can be easily converted to m/s by dividing for 36 (e.g. 18/36 = 0.5 m/s). - **Trial Number**: A string ID for each 30-second trial (e.g., `"1"`, `"2"`, etc.)</p> <p>-Each trial entry contains a dictionary of 56 sampled variables over time: Frame and Subframe are index vars - The following variables are grouped by three, and named as right or left (R, L) + the name of the body marker + the axis (e.g. RLOBx, RLOBy, RLOBz, for a total of 9 anatomical positions and 18 markers). LOB = Temporal lobe; SH = Acromion; ELB = Elbow; WR = Ulnar stiloyd process; GT = Great Trochanter; KN = Knee (lateral); AN = Malleolus lateral; HEE = Heel; MT = 5th Metatarsal. The 3D space was calibrated following the right hand rule, with the axis "X" = mediolateral; "Y" = anteroposterior; "Z" = vertical. Values are in mm.</p> <p><b> Subjects_groups_db (CSV File)</b></p> <p>This table contains the participants' data</p> --- </head> <body> <p><b> Example </b></p> <pre><code>{ { "7": { "45": { "1": { "Frame": [1, 2, 3, ...], "Subframe": [0, 0, 0, ...], "RLOBx": [61.44, 61.92, 60.88, ...], "RLOBy": [56.09, 56.09, 54.58, ...], ... } } } }</code></pre> </body> </html> |
| eu_rights_str_mv | openAccess |
| format | dataset |
| id | anni_32a02465662baad1f8c3a5c873bce919 |
| instacron_str | Agencia Nacional de Investigación e Innovación |
| institution | Agencia Nacional de Investigación e Innovación |
| instname_str | Agencia Nacional de Investigación e Innovación |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | doi:10.60895/redata/SB0OXW |
| publishDate | 2025 |
| publishDateSort | 2025 |
| publisher.none.fl_str_mv | Repositorio de datos abiertos de investigación de Uruguay |
| reponame_str | REDATA |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control ParticipantsBiancardi, CarloBona, RenataGianneechini, GonzaloMedicine, Health and Life SciencesType 2 Diabetes MellitusClinical gait analysisMotion Capture System<p>The dataset includes kinematics data recorded by a Motion Capture System (Vicon Motion System, Oxford, UK) at 100Hz. Data were filtered (fourth-order zero-lag Butterworth low-pass filter with a cutoff frequency of 6 Hz) and exported with Vicon’s Nexus 2.15 software.</p> <p><b> DM2_kinematics_data (JSON Dataset)</b></p> <p><b> CG_kinematics_data (JSON Dataset)</b></p> <p>These datasets contain walking trials data collected across 37 subjects: 15 with type 2 diabetes mellitus (DM2) and 22 healthy participants (CG). The groups were paired for age and sex. Each participant performed 5 sessions on a treadmill at different constant speeds: the individual's self-selected walking speed (SSWS), two speeds above and two speeds below the SSWS. During each session, up to three 30-second trials were recorded by the MOCAP system. The data is stored as a hierarchical JSON file for ease of parsing, analysis, and expansion.</p> --- <p><b> Structure</b></p> <p>The data is organized in four hierarchical levels:</p> <p>Subject ID → Speed → Trial Number → Kinematic Variables (markers 3D position)</p> <p>Where:</p> <p>-- **Subject ID**: A numeric string (e.g., `"7"`), - **Speed**: Integer string representing the gait speedin hm/h (e.g., `"18"` means 1.8 km/h). Speed values can be easily converted to m/s by dividing for 36 (e.g. 18/36 = 0.5 m/s). - **Trial Number**: A string ID for each 30-second trial (e.g., `"1"`, `"2"`, etc.)</p> <p>-Each trial entry contains a dictionary of 56 sampled variables over time: Frame and Subframe are index vars - The following variables are grouped by three, and named as right or left (R, L) + the name of the body marker + the axis (e.g. RLOBx, RLOBy, RLOBz, for a total of 9 anatomical positions and 18 markers). LOB = Temporal lobe; SH = Acromion; ELB = Elbow; WR = Ulnar stiloyd process; GT = Great Trochanter; KN = Knee (lateral); AN = Malleolus lateral; HEE = Heel; MT = 5th Metatarsal. The 3D space was calibrated following the right hand rule, with the axis "X" = mediolateral; "Y" = anteroposterior; "Z" = vertical. Values are in mm.</p> <p><b> Subjects_groups_db (CSV File)</b></p> <p>This table contains the participants' data</p> --- </head> <body> <p><b> Example </b></p> <pre><code>{ { "7": { "45": { "1": { "Frame": [1, 2, 3, ...], "Subframe": [0, 0, 0, ...], "RLOBx": [61.44, 61.92, 60.88, ...], "RLOBy": [56.09, 56.09, 54.58, ...], ... } } } }</code></pre> </body> </html>Repositorio de datos abiertos de investigación de UruguayBiancardi, Carlo2025-09-08Datasethttp://purl.org/coar/resource_type/c_ddb1info:eu-repo/semantics/datasethttps://doi.org/10.60895/redata/SB0OXWreponame:REDATAinstname:Agencia Nacional de Investigación e Innovacióninstacron:Agencia Nacional de Investigación e InnovaciónBreath-by-breath cardiometabolic data during walking in type 2 diabetes and control participants https://doi.org/10.60895/redata/D2I4UJinfo:eu-repo/semantics/openAccessdoi:10.60895/redata/SB0OXW2026-06-17T23:09:06Z |
| spellingShingle | Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control Participants Biancardi, Carlo Medicine, Health and Life Sciences Type 2 Diabetes Mellitus Clinical gait analysis Motion Capture System |
| title | Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control Participants |
| title_full | Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control Participants |
| title_fullStr | Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control Participants |
| title_full_unstemmed | Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control Participants |
| title_short | Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control Participants |
| title_sort | Open Kinematic Dataset of Walking at Different Speeds in Type 2 Diabetes and Control Participants |
| topic | Medicine, Health and Life Sciences Type 2 Diabetes Mellitus Clinical gait analysis Motion Capture System |
| url | https://doi.org/10.60895/redata/SB0OXW |