The video demonstrates an accurate, low-latency body tracking approach for VR-based applications using Vive Trackers. Using a HTC Vive headset and Vive Trackers, an immersive VR experience, by animating the motions of the avatar as smoothly, rapidly and as accurately as possible, has been created. The user can see her from the first-person view.


Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.


Human Neck movements data acquired using Meatwear - CPRO device - Accelerometer-based Kinematic data. Data fed to OpenSim simulation software extracted Kinematics and Kinetics (Muscles, joints - Forces, Acceleration, Position)


The dataset is part of the MIMIC database and specifically utilise the data corresponding to two patients with ids 221 and 230.


The purpose of this data collection was for the validation of a cuffless blood pressure estimation model during activities of daily living. Data were collected on five young healthy individuals (four males, age 28 ± 6.6 yrs) of varied fitness levels, ranging from sedentary to regularly active, and free of cardiovascular and peripheral vascular disease. Arterial blood pressure was continuously measured using finger PPG (Portapres; Finapres Medical Systems, the Netherlands).


The PD-BioStampRC21 dataset provides data from a wearable sensor accelerometry studyconducted for studying activity, gait, tremor, and other motor symptoms in individuals with Parkinson's disease (PD).In addition to individuals with PD, the dataset also includes data for controls that also went through the same study protocol as the PD participants. Data were acquired using lightweight MC 10 BioStamp RC sensors (MC 10 Inc, Lexington, MA), five of which were attached to each participant for gathering data over a roughly two day interval.


Users of the dataset should cite the following paper:

Jamie L. Adams, Karthik Dinesh, Christopher W. Snyder, Mulin Xiong, Christopher G. Tarolli, Saloni Sharma, E. Ray Dorsey, Gaurav Sharma, "A real-world study of wearable sensors in Parkinson’s disease". Submitted.

where an overview of the study protocol is also provided. Additional detail specific to the dataset and file naming conventions is provided here.

The dataset is comprised of two main components: (I) Sensor and UPDRS-assessment-task annotation data for each participant and (II) demographic and clinical assessment data for all participants. Each of these is described in turn below:

I) Sensor and UPDRS-assessment-task annotation data:

For each participant the sensor accelerometry  and UPDRS-assessment-task annotation data are provided as a zip file, for instance, for participant ID 018. Unzipping the file generates a folder with a name matching the participant ID, for example, 018, that contains the data organized as the following files. Times and timestamps are consistently reported in units of milliseconds starting from the instant of the earliest sensor recording (for the first sensor applied to the participant).

a) Accelerometer sensor data files (CSV) corresponding to the five different sensor placement locations, which are abbreviated as

   1) Trunk (chest)                  - abbreviated as "ch"

   2) Left anterior thigh           - abbreviated as "ll"

   3) Right anterior thigh        - abbreviated as "rl"

   4) Left anterior forearm      - abbreviated as "lh"

   5) Right anterior forearm    - abbreviated as "rh"

   Example file name for accelerometer sensor data files:


   E.g. ch_ID018Accel.csv, ll_ID018Accel.csv, rl_ID018Accel.csv, lh_ID018Accel.csv, and rh_ID018Accel.csv

   File format for the accelerometer sensor data files: Comprises of four columns that provide a timestamp for    each measurement and corresponding triaxial accelerometry relative to the sensor coordinate system.

   Column 1: "Timestamp (ms)"             - Time in milliseconds

   Column 2: "Accel X (g)"                      - Acceleration in X-direction (in units of g = 9.8 m/s^2)

   Column 3: "Accel Y (g)"                      - Acceleration in Y-direction (in units of g = 9.8 m/s^2)

   Column 4: "Accel Z (g)"                      - Acceleration in Z-direction (in units of g = 9.8 m/s^2)

b) Annotation file (CSV). This file provides tagging annotations for the sensor data that identify, via start and end timestamps,     the durations of various clinical assessments performed in the study.   

   Example file name for annotation file:


   E.g. AnnotID018.csv 

    File format for the annotation file: Comprises of four columns

   Column 1: "Event Type"                      - List of in-clinic MDS-UPDRS assessments. Each assessment comprises of                                                                 two queries -  medication status and MDS-UPDRS assessment body locations

   Column 2: "Start Timestamp (ms)"     - Start timestamp for the MDS-UPDRS assessments

   Column 3: "Stop Timestamp (ms)"      - Stop timestamp for the MDS-UPDRS assessments

   Column 4: "Value"                               - Responses to the queries in Column 1 - medication status (OFF/ON) and                                                                  MDS-UPDRS assessment body locations (E.g. RIGHT HAND, NECK, etc.)

   II) Demographic and clinical assessment data

For all participants, the demographic and clinical assessment data are provided as a zip file "". Unzipping the file generates a CSV file named Clinic_DataPDBioStampRCStudy.csv.

File format for the demographic and clinical assessment data file: Comprises of 19 columns

Column 1: "ID"                                                                              - Participant ID

Column 2: "Sex"                                                                            - Participant sex (Male/Female)

Column 3: "Status"                                                                        - Participant disease status (PD/Control)

Column 4: "Age"                                                                            - Participant age

Column 5: "updrs_3_17a"                                                              - Rest tremor amplitude (RUE - Right Upper Extremity)

Column 6: "updrs_3_17b"                                                              - Rest tremor amplitude (LUE - Left Upper Extremity)

Column 7: "updrs_3_17c"                                                              - Rest tremor amplitude (RLE - Right Lower Extremity)

Column 8: "updrs_3_17d"                                                              - Rest tremor amplitude (LLE - Right Lower Extremity)

Column 9: "updrs_3_17e"                                                              - Rest tremor amplitude (Lip/Jaw)

Column 10 - Column 14: "updrs_3_17a_off" - "updrs_3_17e_off"  - Rest tremor amplitude during OFF medication assessment                                                                                                         (ordering similar as that from Column 5 to Column 9)

Column 15 - Column 19: "updrs_3_17a_on" - "updrs_3_17e_on"   - Rest tremor amplitude during ON medication assessment

For details about different MDS-UPDRS assessments and scoring schemes, the reader is referred to:

Goetz, C. G. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23, 2129-2170, doi:10.1002/mds.22340 (2008)   


Falls are a major health problem with one in three people over the age of 65 falling each year, oftentimes causing hip fractures, disability, reduced mobility, hospitalization and death. A major limitation in fall detection algorithm development is an absence of real-world falls data. Fall detection algorithms are typically trained on simulated fall data that contain a well-balanced number of examples of falls and activities of daily living. However, real-world falls occur infrequently, making them difficult to capture and causing severe data imbalance.


Follow instruction in readme file


The goal of this study was to compute the relative angle of human joints such as the knee flex/extension angle using two IMUs. To do so, we utilized two 6-axis (accelerometer, gyroscope) low-cost IMUs (MPU6050, TDK-Invensense, CA, USA) that were mounted on a custom developed test apparatus that replicated the human knee motion.


In this dataset, there is a total of 45 files. 



9 raw readings of the nine test trials (text files (.txt)) (e.g., raw_yaw_slow.txt)


Each trial contained 22 columns of data. Each column of data contains the following. 


1) Sampled Time (ms)


2) Encoder (deg)


3,4,5,6) IMU 2 quaternion from DMP (4 values = a,b,c,d)


7,8,9,10) IMU 1 quaternion from DMP (4 values = a,b,c,d)


11,12,13) Raw Gyroscope Readings (deg/s) of IMU 2 about x,y,z (3 values) 


14,15,16) Raw Accelerometer Readings (g - gravitational constant) of IMU 2 about x,y,z (3 values)


17,18,19) Raw Gyroscope Readings (deg/s) of IMU 1 about x,y,z (3 values) 


20,21,22) Raw Accelerometer Readings (g - gravitational constant) of IMU 1 about x,y,z (3 values)




MATLAB scripts are used to process the raw text files. See the documentation for MATLAB scripts"README_MATLAB_Files.txt"). Additional 36 .mat files will be created. Description for these .mat files are given below. 


9 raw readings of the nine test trials in MATLAB data files (.mat)  (e.g., raw_yaw_slow.mat)


These files are similar to the previous raw text files but converted into MATLAB data space for easier processing in MATLAB.


These contain similar information as the previous raw text files as well as the line of when the calibration phase and data phase starts.   




9 filtered readings of the nine test trails in MATLAB data files (.mat) (e.g., filtered_yaw_slow.mat)


These files contain filtered readings of the previous raw MATLAB files. Any missing data strings are removed. A 4th order Butterworth low-pass filter at 4 Hz is applied to the raw IMU data. 




9 angle readings of the nine test trials in MATLAB data files (.mat)  (e.g., raw_yaw_slow.mat) (e.g., angle_yaw_slow.mat)


These files contain computed angles from the previous filtered MATLAB files. Five computational algorithms (Accelerometer Inclination, Gyroscopic Integration, Complementary Filter, Kalman Filter, and Digital Motion Processing) are used. For more info on these algorithms, see 




9 metric readings of the nine test trials in MATLAB data files (.mat)  (e.g., raw_yaw_slow.mat) (e.g., metric_yaw_slow.mat)


These files contain computed metrics such as Root-Mean-Squared-Errors (RMSE) for each computed algorithm. The RMSE is a metric for how accurately each algorithm can estimate the true rotated angle. A RMSE below 6 degrees is acceptable for quantifying human joint angles in biomechanics. 








It contains the four biomarkers which we have selected for the instrument, in the first column we have the recordings for heart, in second we have recordings for temperature, third is for muscle activity and last column is for oxygen levels.


GesHome dataset consists of 18 hand gestures from 20 non-professional subjects with various ages and occupation. The participant performed 50 times for each gesture in 5 days. Thus, GesHome consists of 18000 gesture samples in total. Using embedded accelerometer and gyroscope, we take 3-axial linear acceleration and 3-axial angular velocity with frequency equals to 25Hz. The experiments have been video-recorded to label the data manually using ELan tool.