Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables

Abstract

Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. The effort required to obtain and annotate such datasets is massive, therefore we propose a smart annotation pipeline which reduces the number of events needing manual adjustment to 14%. For scenarios dominated by walking, this annotation effort is as low as 8%. The pipeline consists of three smart annotation approaches, namely edge detection of the pressure data, local cyclicity estimation, and iteratively trained hierarchical hidden Markov models. Using this pipeline, we have collected and labeled a dataset with over 150,000 labeled cycles, each with 2 phases, from 80 subjects, which we have made publicly available. The dataset consists of 12 different task-driven activities, 10 of which are cyclic. These activities include not only straight and steady-state motions, but also transitions, different ranges of bouts, and changing directions. Each participant wore 5 synchronized inertial measurement units (IMUs) on the wrists, shoes, and in a pocket, as well as pressure insoles and video. We believe that this dataset and smart annotation pipeline are a good basis for creating a benchmark dataset for validation of other semi- and unsupervised algorithms.

Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients

“…However, independent of the recording environment, a robust segmentation of individual strides from the continuous sensor data is one of the first steps in most wearable gait analysis systems and a crucial part of the underlying signal processing pipeline [7]. Various different approaches have been proposed in the literature to solve the problem of stride segmentation for clinical gait analysis applications [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Methods vary with sensor location, ranging from the upper body like wrist, chest or lower back [9,10] to the lower body with sensors attached to ankles or feet [11][12][13][14][15][17][18][19][20][21][22] as well as with sensor modalities like IMUs or pressure sensors [7].…”

Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System

“…The FAU dataset is based on a previous study evaluating a method for smart labeling of cyclic activities [24] and is publicly available at www.activitynet.org. The dataset provides gait data in a relatively natural setting, and its protocol consisted in the execution of 12 different task-driven activities performed in random order for each participant.…”

“…After the detection of all the TOs that belong to the activity, all the portions corresponding from TO to TO are considered as strides (Figure 6b). Considering that the stride time of walking strides is around one second [24], if one TO to TO portion is greater than 2 s (400 samples), only the signal until 1.5 s was taken into account. This often happens because the participant is standing still or sitting.…”

Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor

“…In fact, people perform lower limb locomotion activities every day, such as moving from one place to another place and doing sports like running and cycling. … There are a lot of methods that have been proposed for HAR, while to our best knowledge, very few methods can be found that are specially designed for lower limb locomotion activities, including but not limited to activities like walking and jogging [25].…”