Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

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Biomechanics And Motor Control Of Human Movement

In this chapter, you will see how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models.” You will also see that if a network flow model has “integer-valued data,” the simplex method finds an optimal solution that is integer-valued.

Flows in Networks

Gait Analysis. An Introduction

Smartphone pedometry offers the possibility of ubiquitous health monitoring, context awareness and indoor location tracking through Pedestrian Dead Reckoning (PDR) systems. However, there is currently no detailed understanding of how well pedometry works when applied to smartphones in typical, unconstrained use. This paper evaluates common walk detection (WD) and step counting (SC) algorithms applied to smartphone sensor data. Using a large dataset (27 people, 130 walks, 6 smartphone placements) optimal algorithm parameters are provided and applied to the data. The results favour the use of standard deviation thresholding (WD) and windowed peak detection (SC) with error rates of less than 3%. Of the six different placements, only the back trouser pocket is found to degrade the step counting performance significantly, resulting in undercounting for many algorithms.

Walk detection and step counting on unconstrained smartphones

After decades of evolution, measuring instruments for quantitative gait analysis have become an important clinical tool for assessing pathologies manifested by gait abnormalities. However, such instruments tend to be expensive and require expert operation and maintenance besides their high cost, thus limiting them to only a small number of specialized centers. Consequently, gait analysis in most clinics today still relies on observation-based assessment. Recent advances in wearable sensors, especially inertial body sensors, have opened up a promising future for gait analysis. Not only can these sensors be more easily adopted in clinical diagnosis and treatment procedures than their current counterparts, but they can also monitor gait continuously outside clinics – hence providing seamless patient analysis from clinics to free-living environments. The purpose of this paper is to provide a systematic review of current techniques for quantitative gait analysis and to propose key metrics for evaluating both existing and emerging methods for qualifying the gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications.

Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review

The fidelity of many inertial Body Sensor Network (BSN) applications depends on accurate spatio-temporal information retrieved from body-worn devices. However, there are many challenges caused by inherent sensor errors in inertial BSNs and the uncertainty of dynamic human motion in various situations, such as integration drift and mounting error. Spatial information is especially difficult to extract from inertial data. This paper presents practical methods to minimize errors caused by these challenges within the context of a case study -- gait speed estimation -- where both temporal and spatial information are crucial for accuracy. These methods include a practical calibration procedure for correcting mounting error in order to obtain more accurate spatial information and a refined human gait model for more accurate temporal information.

Extracting Spatio-Temporal Information from Inertial Body Sensor Networks for Gait Speed Estimation

Body sensor networks (BSNs) have provided the opportunity to monitor energy expenditure (EE) in daily life and with that information help reduce sedentary behavior and ultimately improve human health. Current approaches for EE estimation using BSNs require tedious annotation of activity types and multiple body sensor nodes during data collection and high accuracy activity classifiers during post processing. These drawbacks impede deploying this technology in daily life — the primary motivation of using BSNs to monitor EE. With the goal of achieving the highest EE estimation accuracy with the least invasiveness and data collection effort, this paper presents an unsupervised, single-node solution for data collection and activity clustering. Motivated by a previous finding that clusters of similar activities tend to have similar regression models for estimating EE, we apply unsupervised clustering to implicitly group activities with homogeneous features and generate specific regression models for each activity cluster without requiring manual annotation. The framework therefore does not require specific activity classification, hence eliminating activity type labels. With leave-one-subject-out cross-validation across 10 subjects, an RMSE of 0.96 kcal/min was achieved, which is comparable to the activity-specific model and improves upon a single regression model.

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Unsupervised activity clustering to estimate energy expenditure with a single body sensor

Body sensor network (BSN) applications depend on accurate and precise data from body-worn devices, but issues related to sensor variations, body mounting variations, and node-to-node synchronization can dramatically impact the quality and reliability of collected data and, ultimately, application fidelity. Characterizing and addressing these sources of error -- which are both static and dynamic (e.g. sensors suffer from static manufacturing variability and dynamic environmental impacts) -- within the context of application requirements is therefore necessary for the viability of such applications.This work characterizes and addresses errors related to sensor and mounting calibration and node synchronization on a case study application -- knee joint angle as measured during walking by an accelerometer- and gyroscope-based BSN. Using an industrial optical motion capture system to provide ground truth, calibration and synchronization error are quantified and the efficacy of solutions for reducing such errors are evaluated.

Characterizing and minimizing synchronization and calibration errors in inertial body sensor networks

Ankle-foot orthoses (AFOs) are often prescribed to individuals with walking disabilities, including children with cerebral palsy. Despite the widespread use of AFOs, their efficacy is not well evaluated because the quantitative assessment of gait improvement from AFOs is currently limited to short-term, in-clinic observation. To better understand how AFOs perform in aiding individuals with walking disabilities and to further enhance their efficacy, longitudinal, continuous, non-invasive measurement is necessary.Ankle joint angle is a key parameter impacted by the AFO and is central to assessing AFO efficacy. With a wireless inertial body sensor network (BSN) mounted on -- or even embedded in -- the AFOs, the ankle joint angle can be extracted and then used to derive other gait parameters such as the range of ankle angle and the percentage of time in dorsi-/plantar-flexion mode. The methodology of extracting ankle joint angle and related gait parameters for assessing AFO efficacy is detailed in this paper. In order to obtain accurate spatial information, techniques for compensating integration drift, mounting error and multi-plane motion are also presented. The BSN results are validated against the industrial standard optical motion capture system on four children with cerebral palsy. An ankle angle RMSE of 2.41 degrees was achieved, demonstrating the potential of using BSNs for longitudinal assessment of AFO efficacy.

Shanshan Chen

Papers

Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review

Extracting Spatio-Temporal Information from Inertial Body Sensor Networks for Gait Speed Estimation

Unsupervised activity clustering to estimate energy expenditure with a single body sensor

Characterizing and minimizing synchronization and calibration errors in inertial body sensor networks

Enabling longitudinal assessment of ankle-foot orthosis efficacy for children with cerebral palsy