HMC2 - Revision history

Slawek at 14:21, 19 May 2014

2014-05-19T14:21:11Z

Siddhartha at 14:07, 19 May 2014

2014-05-19T14:07:52Z

Siddhartha at 13:52, 19 May 2014

2014-05-19T13:52:29Z

Siddhartha at 13:50, 19 May 2014

2014-05-19T13:50:50Z

MediaWiki default at 12:53, 19 May 2014

2014-05-19T12:53:48Z

Siddhartha at 11:07, 19 May 2014

2014-05-19T11:07:39Z

Siddhartha at 09:47, 19 May 2014

2014-05-19T09:47:54Z

Siddhartha at 09:40, 19 May 2014

2014-05-19T09:40:01Z

Siddhartha at 09:37, 19 May 2014

2014-05-19T09:37:49Z

Siddhartha at 09:26, 19 May 2014

2014-05-19T09:26:24Z

← Older revision		Revision as of 14:21, 19 May 2014
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	{{ShowProjectPublications}}		{{ShowProjectPublications}}
		+	Markus Schafflinger (2012). [http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:561635 Quality Assessment of Bicycle Training]. Master Thesis, Halmstad University.

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 == Approach ==
 [[File:Figure_actimeter.png|left,top|thumb|caption|"Figure 1: Estimation of MET (metabolic energy transfer) from accelerometers. The red curve is from the VO2 measurements, each data point is an average of six measurements to give 1 data point per minute. The blue curve is an estimate by the ActiveMeter."]]
 *Inertial Sensors: The approach is to use wearable sensors, typically accelerometers, to estimate motion intensities and qualities and estimate energy consumption. The person wearing the sensors can get immediate (or almost immediate) feedback as well as a time log of energy consumption.
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 A large data collection, involving around 20 subjects, is ongoing. The target is to validate our previously developed algorithm for estimation of energy expenditure for common activities in the low to medium intense activity range. To make the best possible validation, the gold standard for measurement of energy expenditure is used as a reference. Furthermore, several different placement options are selected to measure sensitivity to sensor placement on the body. An example of a pilot measurement is shown in Figure 1, where one subject performed a protocol of different activities. The red curve is measured by the VO2 apparatus (50K+ EUR) whereas the blue curve is estimated by the ActiveMeter (which is developed by members of the project team).
 To develop applications for long term analysis of gait in uncontrolled environments, some of the proposed methods in the literature for estimation of the characteristic events (heel strike and toe-off) in gait is not good enough. A new method based on continuous wavelet transform, see Figure 2, in combination with prior expert knowledge was proposed [2]. The method can robustly find the events for "outdoor walking", even when the data is very noisy due to ground conditions and other movement artifacts.
 The estimated information could not only prove useful for sports applications, optimization of cadence and similar, but also for healthcare applications such as estimating gait variability over time to assess the risk of falling, especially for the elderly.
 === EMG ===

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 == Approach ==
 *Inertial Sensors: The approach is to use wearable sensors, typically accelerometers, to estimate motion intensities and qualities and estimate energy consumption. The person wearing the sensors can get immediate (or almost immediate) feedback as well as a time log of energy consumption.
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 == Results so far ==
 [[File:Figure_wavelet.png|left,top|thumb|caption|"Figure 2.  Identifying the characteristic event, heel strike (HS) and toe-off (TO) in outdoor walking. The top part is the resultant acceleration for well-behaved steps. The bottom part show the sym4 wavelet which matches the HS quite well."]]

← Older revision		Revision as of 13:50, 19 May 2014
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	[[File:Table_EMG.PNG\|left,top\|thumb\|caption\|"Table 1. The coefficient of determination of the model."]]		[[File:Table_EMG.PNG\|left,top\|thumb\|caption\|"Table 1. The coefficient of determination of the model."]]
		+
		+	[[File:Emg.png\|left,top\|thumb\|caption\|"Figure 3. Mean frequency from the RS muscle as a function of time for two participants."]]

	=== Inertial Sensors ===		=== Inertial Sensors ===

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 [[File:Table_EMG.PNG|left,top|thumb|caption|"Table 1. The coefficient of determination of the model."]]
-Inertial Sensors
+=== Inertial Sensors ===
 A large data collection, involving around 20 subjects, is ongoing. The target is to validate our previously developed algorithm for estimation of energy expenditure for common activities in the low to medium intense activity range. To make the best possible validation, the gold standard for measurement of energy expenditure is used as a reference. Furthermore, several different placement options are selected to measure sensitivity to sensor placement on the body. An example of a pilot measurement is shown in Figure 1, where one subject performed a protocol of different activities. The red curve is measured by the VO2 apparatus (50K+ EUR) whereas the blue curve is estimated by the ActiveMeter (which is developed by members of the project team).
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-EMG
+=== EMG ===
 Two subjects participated in the data collection experiment conducted at the facilities of the Sports Academy of University of Gothenburg. Progressively increasing and constant loads were used and EMG signals were recorded from four muscles of both legs, eight channels in total. In addition to EMG signals, readings from accelerometers attached to the legs and chest, as well as measurements of heart rate, oxygen consumption, lactate concentration in blood, cadence and subjective evaluations of fatigue were also taken. Data collection activities have been continued in December 2013 at Halmstad University using the same protocol with the exception that the load was kept constant.

← Older revision		Revision as of 11:07, 19 May 2014
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	[[File:Figure_wavelet.png\|left,top\|thumb\|caption\|"Figure 2. Identifying the characteristic event, heel strike (HS) and toe-off (TO) in outdoor walking. The top part is the resultant acceleration for well-behaved steps. The bottom part show the sym4 wavelet which matches the HS quite well."]]		[[File:Figure_wavelet.png\|left,top\|thumb\|caption\|"Figure 2. Identifying the characteristic event, heel strike (HS) and toe-off (TO) in outdoor walking. The top part is the resultant acceleration for well-behaved steps. The bottom part show the sym4 wavelet which matches the HS quite well."]]
		+
		+	[[File:Table_EMG.PNG\|left,top\|thumb\|caption\|"Table 1. The coefficient of determination of the model."]]

	Inertial Sensors		Inertial Sensors

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 The estimated information could not only prove useful for sports applications, optimization of cadence and similar, but also for healthcare applications such as estimating gait variability over time to assess the risk of falling, especially for the elderly.
 EMG
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 *however, information is present in signal variability and distribution of signal energy (relatively high prediction accuracy shown in Table 1)
-[[File:poster_actimeter.jpg||right,top|<span title=""></span>]]
 {{ShowProjectPublications}}

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 |ProjectEnd=2015/12/31
 |ApplicationArea=Health Technology
-|Lctitle=Yes
+|Lctitle=No
 }}
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