The dataset for the challenge, "Understanding Normal vs. Unusual Activities Related to Parkinson's Disease with Multimodal Data," has been collected using various sensors and mobile applications to ensure a comprehensive multimodal approach to activity recognition and classification.
Parkinson.movSensors
Smartwatch
Captures motion and activity data, including accelerometer, gyroscope, and other sensor outputs relevant for detecting tremors and other motor abnormalities.
Mobile Phone’s Accelerometer
Provides additional motion data, allowing fine-grained activity tracking and redundancy for improved data reliability.
Skeleton Joints (Pose Estimation)
Extracts spatial and temporal joint movement data, which is particularly useful for identifying postural changes and movement irregularities.
RGB Data (Not Covered)
While RGB data was initially planned, it is not included in the dataset to focus on sensor and skeletal data.
Applications Used
FonLog (Japan)
A mobile application that logs sensor data and tracks user activities, providing rich contextual information for activities in real-world settings.
Fitrockr (Germany)
An advanced platform for wearable device integration, used for capturing data from smartwatches and other IoT devices with activity monitoring.
Sensor Data:
Main directory: users_timeXYZ/users/
Contains multiple subdirectories with random numerical names (e.g., '38', '1716')
Each subdirectory contains 1/multiple CSV files with accelerometer data
File naming pattern: user-acc_[DIR-NUMBER]_[TIMESTAMP]_[RANDOM-NUMBER].csv
E.g., '38' folder has 5 files:
user-acc_38_2024-09-08T23_31_01.510+0100_97016.csv
user-acc_38_2024-09-08T23_31_16.519+0100_15638.csv
Sensor Data File Format: Each CSV file contains 5 columns:
- Random identifier (to be ignored)
- Timestamp
- x-axis accelerometer data
- y-axis accelerometer data
- z-axis accelerometer data
Activity Labels:
(FACING camera) Sit and stand
(FACING camera) both hands SHAKING (sitting position)
Stand up from chair - both hands with SHAKING
(Sideway) Sit & stand
(Sideway) both hands SHAKING (sitting)
(Sideway) STAND up with - both hands SHAKING
Cool down - sitting/relax
Walk (LEFT --> Right --> Left)
Walk & STOP/frozen, full body shaking, rotate then return back
Slow walk (SHAKING hands/body, tiny step, head forward)
File: TrainActivities.csv: Contains 7 columns:
- ID (random identifier)
- Activity Type ID
- Activity Type (10 distinct activity classes): You need to recognize them.
- Start Time
- End Time
- Update Time
- Subject ID (e.g., U1, U2, U3, ..., U21, U22)
Training Data:
- 9 subjects provided for training. 9 subjects are: U1, U2, U3, U4, U5, U6, U7, U21, and U22.
- Each subject performs all 10 activities unless any missing data.
- Most activities have multiple repetitions.
Note: Some time gaps may exist due to data collection issues
Key Tasks:
Objective: Develop a model to recognize 10 different activities based on accelerometer data.
- Link accelerometer data with corresponding activities and subjects
- Develop recognition model(s) for the 10 activity classes
- Implement appropriate train/test splitting strategies
- Document your approach and results so that you can write a paper
Data Set Background:
The dataset captures a range of activity classes that include both normal and unusual activities associated with Parkinson’s disease. These classes are designed to help identify and differentiate patterns of movement and behaviors.
Relax Time
Represents periods of rest or minimal physical activity.
Normal Sit and Stand
Routine activities of sitting down and standing up, performed without motor abnormalities.
Tremor While Seated
Involuntary and rhythmic shaking of body parts, such as hands or legs, occurring while the subject is seated.
Indicates motor impairments specific to Parkinson’s disease.
Tremor While Standing
Similar to seated tremors, this involves involuntary movements but occurs while the subject is in a standing position.
Provides data on balance and postural stability under motor impairment conditions.
Normal Walking
Regular walking pattern without disruptions or irregularities.
Acts as a baseline for comparing other gait-related abnormalities.
Freezing and Festinating Gait
Freezing Gait: Episodes where movement temporarily halts, making it difficult to initiate or continue walking.
Festinating Gait: Accelerated, short steps often leading to instability.
Both are critical indicators of advanced Parkinson’s disease.
Shuffling Gait
A distinctive walking pattern characterized by dragging feet or taking short steps.
Common in individuals with Parkinson’s disease and is a marker of progression.
Significance
This dataset is uniquely positioned to offer insights into the daily activities of individuals with Parkinson's disease by leveraging multimodal data sources. The combination of wearable devices, skeleton tracking, and application-based logging ensures diverse and high-quality data for understanding normal and unusual activity patterns.
By integrating data from different sensors, this dataset provides a valuable resource for developing and testing machine learning models that can identify and classify activities with potential applications in disease monitoring and patient care.
Data Sharing Policy
The dataset will be provided exclusively to registered participating teams and is strictly limited to use for the challenge.
