A Method for Extracting Temporal Parameters Based on Hidden Markov Models in Body Sensor Networks With Inertial Sensors

  title={A Method for Extracting Temporal Parameters Based on Hidden Markov Models in Body Sensor Networks With Inertial Sensors},
  author={Eric Guenterberg and Allen Yuqing Yang and Hassan Ghasemzadeh and Roozbeh Jafari and Ruzena Bajcsy and S. Shankar Sastry},
  journal={IEEE Transactions on Information Technology in Biomedicine},
Human movement models often divide movements into parts. In walking, the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into sections based on the primary direction of motion. These parts are often divided based on key events, also called temporal parameters. When analyzing a movement, it is important to correctly locate these key events, and so automated techniques are needed. There exist many methods for dividing specific actions using… 

A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope

  • A. ManniniA. Sabatini
  • Computer Science
    2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 2011
An application of hidden Markov models (HMMs) to the problem of time-locating specific events in normal gait movement patterns through segment gait data collected at different walking speeds and inclinations of the walking surface.

A hidden Markov model for detection and classification of arm action in cricket using wearable sensors

Use of HMM for detection and classification of arm action in the game of cricket is proposed and has applications in cricket coaching and technique adaptation both for novice and trained players.

Hidden Markov model-based strategy for gait segmentation using inertial sensors: Application to elderly, hemiparetic patients and Huntington's disease patients

A solution to discriminate stance and swing in both healthy and abnormal gait using inertial sensors is proposed. The method is based on a two states hidden Markov model trained in a supervised way.

Evaluation of Inertial Sensor Configurations for Wearable Gait Analysis

This paper aims to investigate the effect of type, number and location of inertial sensors on gait detection, so as to offer some suggestions for optimal sensor configuration.

Online Decoding of Hidden Markov Models for Gait Event Detection Using Foot-Mounted Gyroscopes

This paper presents an approach to the online implementation of a gait event detector based on machine learning algorithms that overcame the limitation of the standard Viterbi algorithm on the online decoding of hidden state sequences.

A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington’s Disease Patients

The objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits and the long-term goal is the gait assessment in everyday life to early detect gait alterations.

Algorithm for Temporal Gait Analysis Using Wireless Foot-Mounted Accelerometers

We present a new signal processing algorithm that extracts five gait events: heel strike, toe strike, heel-off, toe-off, and heel clearance from only two accelerometers attached on the heels of the

Automatic Segmentation and Recognition in Body Sensor Networks Using a Hidden Markov Model

This work presents a technique inspired by continuous speech recognition that combines segmentation and classification using hidden Markov models and shows the results of this technique and the bandwidth savings over full data transmission.



A hidden Markov model-based stride segmentation technique applied to equine inertial sensor trunk movement data.

Body posture identification using hidden Markov model with a wearable sensor network

A networked proximity sensing and Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of body postures using a wearable sensor network is presented.

Assessment of walking features from foot inertial sensing

It is concluded that foot inertial sensing is a promising tool for the reliable identification of subsequent gait cycles and the accurate assessment of walking speed and incline.

Capturing human motion using body‐fixed sensors: outdoor measurement and clinical applications

The possibility to detect useful human motion based on new techniques using different types of body‐fixed sensors is shown and a combination of accelerometers and angular rate sensors (gyroscopes) showed a promising design for a hybrid kinematic sensor measuring the 2D kinematics of a body segment.

Gait analysis for recognition and classification

  • L. LeeW. Grimson
  • Computer Science
    Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition
  • 2002
This work describes a representation of gait appearance based on simple features such as moments extracted from orthogonal view video silhouettes of human walking motion that contains enough information to perform well on human identification and gender classification tasks.

A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package

A basic requirement in virtual environments is the tracking of objects, especially humans. A real time motion-tracking system was presented and evaluated in this paper. System sensors were built

Human Motion Analysis: A Review

The paper gives an overview of the various tasks involved in motion analysis of the human body, and focuses on three major areas related to interpreting human motion: motion analysis involving human body parts, tracking of human motion using single or multiple cameras, and recognizing human activities from image sequences.

Long-term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope

  • S. Miyazaki
  • Engineering
    IEEE Transactions on Biomedical Engineering
  • 1997
The purpose of this study was to develop a device with the following design criteria: lightweight, easy attachment, little hindrance to the natural gait pattern, sufficient memory to record for one day, and practicality in clinical use.

Human motion capture by integrating gyroscopes and accelerometers

This paper describes a motion capturing system for human arm motion in real time; directly, simply and precisely by integrating two types of sensor; gyroscopes and accelerometers. A new sensor fusion