WO2015058923A1 - Dispositif et procédé d'estimation de la dépense énergétique d'un individu - Google Patents

Dispositif et procédé d'estimation de la dépense énergétique d'un individu Download PDF

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WO2015058923A1
WO2015058923A1 PCT/EP2014/070599 EP2014070599W WO2015058923A1 WO 2015058923 A1 WO2015058923 A1 WO 2015058923A1 EP 2014070599 W EP2014070599 W EP 2014070599W WO 2015058923 A1 WO2015058923 A1 WO 2015058923A1
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Prior art keywords
heart rate
sensor
person
signal
energy expenditure
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PCT/EP2014/070599
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English (en)
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Sandrine Magali Laure Devot
Koen Theo Johan De Groot
Alberto Giovanni BONOMI
Emile Josephus Carlos Kelkboom
Francesco SARTOR
Antonius Hermanus Maria Akkermans
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Koninklijke Philips N.V.
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Publication of WO2015058923A1 publication Critical patent/WO2015058923A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4866—Evaluating metabolism
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1123—Discriminating type of movement, e.g. walking or running
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention relates to a device and method for estimating the energy expenditure of a person.
  • Physical activity can reduce the risk of developing certain diseases, such as obesity or hypertension and improve the overall well-being and quality of life of a person.
  • An accurate and objective quantification of the physical activity is therefore becoming more and more essential, especially to support public health research and intervention programs and clarify the dose-response relation between physical activity and health.
  • the use of accelerometers is very common to predict energy expenditure, as e.g. described in Bonomi AG, W. K. (2011), Advances in physical activity monitoring and lifestyle, International Journal of Obesity, 1-11. They are cheap, simple to use and wearable. However, it is commonly admitted that some physical activities may be misinterpreted by such devices. As an example, it is difficult to accurately estimate energy expenditure during activities such as cycling, in particular for accelerometers worn on the wrist or at the waist.
  • HR heart rate
  • JP 10-179560 A discloses a calorimeter made of an acceleration sensor, a pulse sensor and a signal processing card.
  • the CPU of the signal processing card makes a judgment about an exercising state on the basis of an acceleration signal from the
  • the CPU monitors a change in the number of pulses detected with the pulse sensor, converts measured pluses to the number of pulses per constant time and compares the converted number of pulses with the number of pulses at a normal time (nonexercising time) previously stored in a memory.
  • the CPU calculates the consumed calories (i.e. the energy expenditure) on an average time basis, using a proportional relation the number of pulses to the consumed calories.
  • the CPU judges the exercising state, and calculates a calorie consumed per average time in each exercising state based on the exercising state and speed, and even personal information. In this way the calories consumed during exercise time and a non-exercise time shall be accurately determined.
  • US 20120083705 Al discloses an activity monitoring system and method which is adapted to estimate energy expenditure using either acceleration information only, heart rate data only, or a combination of these data.
  • the prior art method is adapted to select the appropriate energy estimation equations when the heart rate sensor is positioned in a body location for which heart rate data is unreliable (e.g. torso).
  • a device for estimating the energy expenditure of a person comprising:
  • a movement sensor for generating a movement signal
  • a heart rate sensor for detecting the heart rate of the person and for generating a heart rate signal
  • an estimation unit for estimating the energy expenditure of the person from said movement signal and/or said heart rate signal
  • the movement sensor is adapted to detect a physical activity of the person depending on a value of the movement signal
  • the device further comprising: a control unit for switching on the heart rate sensor during a predetermined amount of time corresponding to the detection of physical activity of the person and such that the movement signal and/or the heart rate signal is used for estimating the energy expenditure, and for switching off the heart rate sensor otherwise such that only the movement signal is used for estimating the energy expenditure.
  • the predetermined amount of time may correspond to the physical activity period of the person as detected by the movement sensor.
  • the predetermined amount of time is a recovery period starting at the end of the physical activity period of the person and ending at when the heart rate of the person becomes stable.
  • a method for estimating the energy expenditure of a person comprising:
  • estimating the energy expenditure of the person from a movement signal provided by a movement sensor and/or a heart rate signal provided by a heart rate sensor for detecting the heart rate of the person,
  • a corresponding computer program which comprises program code means for causing a computer to perform the steps of the method disclosed herein when said computer program is carried out on a computer as well as a non-transitory computer-readable recording medium that stores therein a computer program product, which, when executed by a processor, causes the method disclosed herein to be performed.
  • the present invention is based on the recognition that combining a movement sensor, such as an accelerometer, with a HR sensor leads to a better estimation of the energy expenditure, but HR sensors can be more power demanding than accelerometers.
  • the addition of a HR sensor leads therefore to a (significant) reduction of the battery life of the whole device.
  • the HR sensor can be prone to motion artefacts, leading to inaccurate HR measurements during the physical activity.
  • the linear relationship between HR and energy expenditure that is used for prediction mostly holds for aerobic activities.
  • switching off the heart rate sensor when appropriate makes it possible to extend the battery life of the HR sensor or even of the complete device, if the battery of the device supplies also the HR sensor.
  • One element of the present invention is therefore to appropriately switch from a movement-based estimation to a HR-based estimation (and vice- versa) so as to take the most out of two methods.
  • the movement-based energy expenditure estimation is used whenever there is a non-physical activity, which is also referred to as the non-activity mode of the estimation unit. This will include low intensity activities, such as sedentary activities, desk work, housework, etc.
  • the HR-based energy expenditure estimation is used whenever there is a high physical activity, which is also referred to as the activity mode of the estimation unit. This will include light to moderate and vigorous physical activity. In this way the accuracy of the energy expenditure estimation is improved.
  • the present invention is further based on the recognition to know when the heart rate signal is less useful in the context of energy expenditure estimation. It is neither generally known nor expected nor obvious that a movement signal of the person is more useful than a HR signal to predict energy (i.e. calories) expenditure during inactivity. During inactivity there is quite a lack of motion so that the movement signal would rather be considered to provide a poor estimation of the energy expenditure. On the other hand, during inactivity the HR signal may suffer from influences related to posture, stress, emotions and others and may thus not accurately reflect the expended energy.
  • the present invention proposes to use the movement signal during inactivity because it generally represents a less power consuming measurement modality than HR signal measurements, at least with many practical embodiments of movement sensors and HR sensors. Further, the selection of the movement signal for use in the estimation of the energy expenditure during inactivity provides a reproducible and stable outcome to the user while measured in absence of motion. On the other hand, the HR signal would fluctuate during inactivity causing a poorly realistic variation in the energy expenditure during rest.
  • a reliability check is introduced, according to which said control unit is configured to control said estimation unit to check the reliability of the heart rate signal and, if physical activity of the person has been detected, to use only the heart rate signal for estimating the energy expenditure if the heart rate signal is considered reliable and to use only the movement signal for estimating the energy expenditure if the heart rate signal is considered unreliable or not available.
  • This reliability check ensures that no noise HR signal measurements are used which would degrade the quality of the energy expenditure estimation.
  • the device further comprises a classification unit for extracting one or more features of the movement signal and to determine if there is physical activity or no physical activity of the person by use of a classification based on the extracted one or more features of the movement signal.
  • Those extracted features may be one or more of the peaks, the intervals between peaks, the rise times, the fall times, the number of peaks, the gradient, the intensity, the duration of peaks, the similitude or correlation between subsequent intervals, the periodicity, and the spectral energy of the movement signal.
  • various algorithms or methods may be used, such as the generally known logistic regression, a neural network, a support vector machine, Naive Bayes, a linear or quadratic discriminant.
  • Said classification unit may further be configured to detect the type of physical activity, in particular to detect a cycling, walking, running or cross-training activity, in which embodiment it is further preferred that said classification unit comprises two or more classifiers, each using a predetermined set of features for detecting a predetermined type of activity. In this way the accuracy of the detection of the activity state of the person can be further improved. For instance, for detecting a cycling activity may be easier by use of a different set of features that are preferably used for detecting a running activity.
  • the knowledge of the type of activity may be used to improve the estimation of the energy expenditure. For instance, a different estimation algorithm may be used.
  • said control unit is configured to switch said estimation unit between a non-activity mode and an activity mode only if the same physical activity status of the person has been detected for a predetermined time period or if for a predetermined percentage of the last number of time windows the same physical activity status of the person has been detected.
  • a smoothing is applied to prevent the estimation unit from switching between the two modes too often and too quickly, which may lead to inaccurate estimations, may even make the estimation impossible or may even damage the estimation unit or the control unit.
  • Preferred embodiments of said movement sensor are an acceleration sensor or a gyroscope or GPS or any tool capable of sensing human purposeful body motion.
  • Preferred embodiments of said heart rate sensor are a photoplethysmograph PPG sensor or an ECG sensor or a heart sound sensor capable of detecting heart beats.
  • Said movement sensor, said heart rate sensor, said estimation unit and/or said control unit preferably all those units, are built into a body worn device, in particular a wristband, wristwatch, smartphone or body belt.
  • a body worn device in particular a wristband, wristwatch, smartphone or body belt.
  • the estimation unit and the control unit which are preferably implemented by a processor and/or by software (such as an app), may be arranged far from the patient, e.g. in a computer, laptop, tablet, smartphone, etc.
  • a connection is established between all the units, e.g. as wireless connection (such as using WLAN or Bluetooth or the like) or as wired connection.
  • the device may also be
  • Fig. 1 shows a diagram illustrating heart rate signal measurements
  • Fig. 2 shows a diagram illustrating results of energy expenditure estimations based on movement signals and based on heart rate signals
  • Fig. 3 shows a schematic diagram of a first embodiment of a device according to the present invention
  • Fig. 4 shows a schematic diagram of a second embodiment of a device according to the present invention
  • Fig. 5 shows a flow chart illustrating a first embodiment of a method according to the present invention
  • Fig. 6 shows a flow chart illustrating a second embodiment of a method according to the present invention
  • Fig. 7 shows a flow chart illustrating how to control activation and deactivation of HR sensor in accordance with an embodiment of the present invention
  • Fig. 8 shows a flow chart illustrating a first part of third embodiment of a method according to the present invention.
  • Fig. 9 shows a flow chart illustrating a second part of third embodiment of a method according to the present invention.
  • HR heart rate
  • HR-based estimation methods may be inaccurate. Most of them assume a constant value for the energy expenditure, namely using a rough estimate of the Basal Metabolic Rate.
  • an individual calibration in a laboratory may be used, but it is a cumbersome procedure, which is not desired for a large-scale consumer product.
  • Fig. 1 showing a diagram illustrating heart rate signal measurements.
  • heart rate signals are shown for various activities taken with a conventional chest belt HR sensor (signal SI), low quality (Q) (S2), medium quality (S3) and high quality (S4).
  • SI chest belt HR sensor
  • Q low quality
  • S3 medium quality
  • S4 high quality
  • SI, S2, and S3 indicate the heart rate signal collected using a HR sensor based on photo-plethysmography.
  • SI, S2, and S3 represent the measured signal in presence of low noise, medium noise or high noise level, which impact the signal quality.
  • a HR sensor is more power demanding than an accelerometer. Therefore, the addition of the HR sensor (significantly) increases the power consumption of the device and leads to a (significant) reduction of its battery life.
  • Fig. 2 showing a diagram illustrating results of energy expenditure estimations based on movement signals ("acceleration-based") and based on heart rate signals ("HR-based”).
  • AEE stands for activity energy expenditure or activity-induced calories expenditure.
  • the AEE error represents the difference between measured and estimated AEE.
  • Fig. 3 shows a schematic diagram of a first embodiment of a device 10a for estimating the energy expenditure of a person according to the present invention. It comprises a movement sensor 12 for detecting physical activity of the person and for generating a movement signal 13, a heart rate sensor 14 for detecting the heart rate of the person and for generating a heart rate signal 15, an estimation unit 16 for estimating the energy expenditure
  • control signal 19 for controlling said estimation unit (by use of a control signal 19) to switch between an non-activity mode in which only the movement signal is used for estimating the energy expenditure if no physical activity of the person has been detected and an activity mode in which the movement signal and/or the heart rate signal is used for estimating the energy expenditure if physical activity of the person has been detected.
  • Fig. 4 shows a schematic diagram of a second embodiment of a device 10b for estimating the energy expenditure of a person according to the present invention.
  • the device 10a it comprises a classification unit 20 for extracting one or more features of the movement signal 13 and to determine if there is physical activity or no physical activity of the person by use of a classification based on the extracted one or more features of the movement signal 13.
  • the control unit 18 and the estimation unit 16 may be implemented by a processor or computer, e.g. as a software program or an app.
  • Said processor may be combined with the movement sensor 12 and the HR sensor 14 into a common device worn by the person, e.g. as wristband, wristwatch or the like.
  • the processor may be arranged away from the person who only carries the movement sensor 12 and the HR sensor 14, which are connected to the processor in any suitable way (wireless or wired).
  • a first embodiment of a method 100 is depicted in Fig. 5.
  • the process takes as input the movement signal 13, in particular an acceleration signal (ACC) recorded during one epoch.
  • a first step S10 carried out by the classification unit 20 or the control unit 18, one or more features of the movement signal 13 are extracted, e.g. based on standard pattern recognition techniques.
  • Such features may include one or more the peaks, the intervals between peaks, the rise times, the fall times, the number of peaks, the gradient, the intensity, the duration of peaks, the similitude or correlation between subsequent temporal intervals, the periodicity, and the spectral energy of the movement signal 13.
  • Example of classification methods are logistic regression, neural networks, support vector machine, Na ' ive Bayes, Linear/Quadratic discriminant, etc. These classifiers can be based on a single feature or on multiple features.
  • a second step S 12 the epoch is classified as C HR if it is recognized as a physical (e.g. aerobic) activity or if not. If the epoch is assigned to the class C HR , the HR- based energy expenditure (EE) prediction shall be used. Otherwise, the acceleration-based energy expenditure prediction shall be used. The process is then repeated for the next epoch.
  • C HR physical (e.g. aerobic) activity or if not.
  • step S14 if the epoch is assigned to the class C HR (i.e. in activity mode) it is checked in step S14 if the HR sensor is switched on. If the HR sensor is switched on the reliability of the HR signal measurement is checked in step SI 6. Checking the reliability of the HR signal prevents from using noisy HR measurements for energy expenditure prediction, increasing therefore the overall accuracy. Different methods can be used to assess the reliability of the HR measurements. In particular, a reliability index can be defined to compare with a pre-defined threshold and ensure a minimum quality. Finally, if the HR signal is found reliable, the energy expenditure is estimated by use of the HR signal in step S I 8. Energy expenditure estimates can be derived from HR according to a model including subjects characteristics providing data on fitness level, gender, basal metabolic rate and resting heart rate.
  • the acceleration signal can be summarized in epochs of fixed lengths and the resulting features often called "activity counts" can be used in linear equations to estimate energy expenditure together with subjects characteristic data such as body weight and height (Bonomi et al., Estimation of Free-Living Energy Expenditure Using a Novel Activity Monitor Designed to Minimize Obtrusiveness Obesity, 2010).
  • step S 14 If it has been found in step S 14 that the HR sensor is not switched on, the HR sensor is now switched on in step S20. It should be noted that, depending on the
  • step S20 when the HR sensors is switched on in step S20, no HR data are available for a certain about of time given the activation time of the HR sensor and the need to compute and average HR data from a sequence of heart beats. Thus, even when the HR sensor is switched on in step S20, the EE estimation can still be based on the acceleration signal in step S26 in a first instance.
  • the HR data is available or a predetermined time after starting the HR measurement, the method continues with step S I 6.
  • step S22 If the epoch is assigned to the class C ACC in step S 12 (i.e. in non-activity mode), it is checked in step S22 it is checked if the HR sensor is switched on. If this is the case the HR sensor is switched off in step S24, where after the energy expenditure is estimated by use of the acceleration signal in step S26. If the HR sensor is already switched off, step S26 is directly carried out.
  • this method provides the advantage of a more accurate estimation of the energy expenditure. Moreover, the on-time of the HR sensor is limited to the periods where the HR-based method is used for energy expenditure estimation, reducing therefore the power consumption and increasing the battery life.
  • the HR-based estimation might use a general equation for all users, or might use 'static' subject characteristics, such as weight, age, sex, BMR, BMI, height or more dynamic characteristics, related to fitness level, such as resting HR, or V0 2 max.
  • the HR-based estimation might also be adjusted by correcting for the individual fitness level, e.g. by monitoring the HR during specific activities and calibrating the equation used for the estimation according to these individual values.
  • FIG. 6 Another embodiment of a method 200 is depicted in Fig. 6. In addition to the steps of the method 100, some further steps are added in this method 200.
  • the HR estimates may not always be available. Following a period with the HR sensor switched off, some delay is expected to get again accurate HR values. An extra step is therefore added to handle the actual on and off switches of the HR sensor. In particular, the availability and reliability of the HR signal measurement during the epoch under consideration is checked. After it has been found in step S14 that the HR sensor is switched on, the HR signal measurement is continued in step S28.
  • An exemplary stability condition 1 checked in step S30 may be that it is checked if there is at least pi % of epochs classified as C HR in the past nl epochs.
  • An exemplary stability condition 2 checked in step S32 may be that it is checked if there is at least p2 % of epochs classified as C ACC in the past n2 epochs.
  • moving windows of length nl epochs and n2 epochs, respectively, is applied and it is looked for a majority of consistently classified epochs.
  • the parameters pi, p2, nl, n2 are tuned depending on the trade-off that is to be achieved.
  • estimation of energy expenditure EE for a given sample is either based on acceleration data or heart rate HR data. It is assumed that acceleration data is available throughout the entire period the system is activated, i.e. the acceleration sensor is always switched-on and exhibits body acceleration data properly.
  • the HR sensor is only activated when needed, mainly to save battery life, and is steered by acceleration data, possibly extended with supplementary data.
  • the decision to deactivate the HR sensor is steered by acceleration and/or HR data, possibly extended with supplementary subject specific data.
  • EE estimation is based on acceleration data, possibly extended with supplementary data.
  • HR data is being analyzed to select only HR information in the EE process at appropriate time instances.
  • Switching between acceleration based EE estimation and HR based EE estimation when the HR sensor is active, is steered by HR data and acceleration data.
  • the mechanism to properly select HR data as modality to estimate EE during physical inactive periods is mainly introduced to estimate accurately burned calories during the excess post- exercise oxygen consumption period and periods with intense physical activities. During the excess post-exercise oxygen consumption period, the body tries to restore itself to its pre- exercise state by consuming oxygen.
  • the classification unit outputs C and the HR sensor outputs HR (if available as HR is not permanently activated) which are delivered to the control unit.
  • C ⁇ Ta the system remains in the default state S30.
  • a timer is reset and starts counting.
  • the system goes immediately to state S33, meaning that the evaluated epoch was assessed as an active period.
  • the resting heart rate Hrest refers to an attribute of subject specific data.
  • Parameter Hrest may be determined during a dedicated protocol upon estimation of caloric expenditure through acceleration and heartbeat information. Moreover, the resting heart rate could be updated by requesting the user to conduct a similar protocol at a later stage.
  • Parameter d could be regarded as a deadband setting to prevent oscillation or repetitive activation-deactivation cycles of the HR sensor when the system is in state S36.
  • FIG. 8 Still another embodiment of a method of estimating energy expenditure is shown in Figs 8 and 9.
  • This embodiment corresponds to a mode where the HR sensor is activated during a predetermined period just after the end of a physical activity.
  • the start of the physical activity is firstly detected in a step S71 by analyzing epochs of the acceleration signal ACC (13).
  • the start time Tstart (75) is issued and the method then seeks for the detection of the end of the physical activity in a step S72.
  • the end time Tend (76) is issued and the HR sensor is turned on in a step S73 and starts measuring the heart rate (HR) during a recovery period to determine HRrecovery (74), the recovery period ending when the heart rate becomes stable.
  • the method can start predicting the expended energy during the activity as shown in Fig. 9.
  • the heart rate value at the end of the activity HRend (82) is estimated in a step S81 based on the HRrecovery measurement (74). This estimation may be required in practice, as it will take a few seconds before the HR- sensor is turned on and starts measuring reliable HR values. Thus, the initial drop of HR during the recovery period is therefore likely to be missed.
  • HR during the activity HR(Tstar Tend) (85) is estimated in a step S84 using as input HRend (82) and the acceleration data during the activity ACC(Tstart:Tend) (83).
  • EE f(HR(Tstart:Tend), auxiliary data.
  • short HR signal probes can also be used as additional features for the classification, which relies then not only on the movement signal. Thereby, the classification accuracy is increased without consuming much more power (depending on the probing frequency).
  • another embodiment comprises two classifiers in a row, the first one focusing on detecting cycling, while the second one performs the classification (as defined before) for the detected non- cycling periods. This allows defining and using movement-based features that are more discriminative to that cycling activity.
  • a classification unit is used that distinguishes between several types of physical activities (e.g. walking, running, cross-training, etc.) for which a HR-based energy expenditure prediction is of advantage.
  • any HR sensor (sometimes also referred to as HR monitor) can be used in combination with a movement sensor (sometimes also referred to as activity monitor).
  • An example of an integrated device which can be used for this purpose is an optical HR sensor containing a photo-plethysmography sensor (for HR detection) and a 3-axial accelerometer (for activity detection) as e.g. described in WO 2013/038296 Al.
  • Such an integrated device may be implemented as a wrist worn device, which can be worn by a user at his arm. This technology allows monitoring HR unobtrusively and comfortably at any location on the body (for example at the wrist). This embodiment is ideal since users will have their fitness evaluation done just by wearing a watch or bracelet throughout the day and possibly night.
  • HR sensors and movement sensors can be used in a device and method according to the present invention as well.
  • HR sensor an electrocardiograph ECG sensor may be used
  • movement sensor one or more motion sensors may be used.
  • HR sensors are photoplethysmograh PPG sensors, and camera PPG sensors and Global Positioning System GPS can be also used as movement sensor.
  • Activity monitoring solutions in the personal health space are oriented towards providing accurate information to the user regarding activity level and energy expenditure with respect to daily targets.
  • the DirectLife service highly relies on the accuracy of the wearable activity monitor to generate user satisfaction during the activity intervention.
  • the device is often used as a carrier of information on the daily achievements. Poor accuracy of the method to assess activity intensity and calories expenditure leads to user dissatisfaction and causes a dramatic decrease in the net promoter score (NPS) for the solution.
  • NPS net promoter score
  • New activity monitors which may use or include the device or method according to the present invention have the potential to provide highly accurate energy expenditure estimates given the ability to record heart rate data together with body movement, in particular body acceleration.
  • the present invention proposes a new method and device to estimate the energy expenditure in daily life and while exercising, using either a movement- based method or an HR-based method and switching from one to the other at appropriate moments in time.
  • the method includes several steps performed at an epoch level. A typical epoch length is one minute.
  • the on-time of the HR sensor is preferably limited to the periods where the HR-based estimation is used, reducing therefore the power consumption and increasing the battery life.
  • the proposed device and method selects which body motion would benefit from HR data for the estimation of energy expenditure by looking at body movement characteristics related to motion rate and intensity.
  • a computer program may be stored/distributed on a suitable non-transitory medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.
  • a suitable non-transitory medium such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

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Abstract

La présente invention concerne un dispositif d'estimation de la dépense énergétique d'un individu avec une précision accrue. Le dispositif proposé comprend un capteur de mouvements (12) pour détecter l'activité physique de l'individu et générer un signal de mouvement (13), un capteur de fréquence cardiaque (14) pour détecter la fréquence cardiaque de l'individu et générer un signal de fréquence cardiaque (15), une unité d'estimation (16) pour estimer la dépense énergique de l'individu à partir dudit signal de mouvement (13) et/ou dudit signal de fréquence cardiaque (15), et une unité de commande (18) pour commander à ladite unité d'estimation (16) de passer d'un mode inactif dans lequel uniquement le signal de mouvement (13) est utilisé pour estimer la dépense énergétique lorsqu'aucune activité physique de l'individu n'a été détectée à un mode actif dans lequel le signal de mouvement (13) et/ou le signal de fréquence cardiaque (15) sont utilisés pour estimer la dépense en énergie si une activité physique de la personne a été détectée.
PCT/EP2014/070599 2013-10-24 2014-09-26 Dispositif et procédé d'estimation de la dépense énergétique d'un individu WO2015058923A1 (fr)

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CN109077710A (zh) * 2017-06-13 2018-12-25 北京顺源开华科技有限公司 自适应心率估计的方法、装置和系统
CN109199327A (zh) * 2017-06-30 2019-01-15 三星电子株式会社 用于连续生物特征监控的自供电可穿戴设备
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CN113171080A (zh) * 2021-04-19 2021-07-27 中国科学院深圳先进技术研究院 一种基于可穿戴传感信息融合的能量代谢评估方法和系统
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US10568549B2 (en) * 2014-07-11 2020-02-25 Amer Sports Digital Services Oy Wearable activity monitoring device and related method
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CN108366742B (zh) * 2015-12-03 2023-08-22 华为技术有限公司 一种生物信号采集方法、装置、电子设备及系统
CN108366742A (zh) * 2015-12-03 2018-08-03 华为技术有限公司 一种生物信号采集方法、装置、电子设备及系统
EP3375357A4 (fr) * 2015-12-03 2018-12-05 Huawei Technologies Co., Ltd. Procédé, dispositif, équipement électronique et système d'acquisition de signaux biologiques
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WO2017108640A1 (fr) * 2015-12-22 2017-06-29 Koninklijke Philips N.V. Dispositif, système et procédé d'estimation de la dépense énergétique d'une personne
CN107405110A (zh) * 2015-12-22 2017-11-28 皇家飞利浦有限公司 用于估计人的能量消耗的设备、系统和方法
JP2017221550A (ja) * 2016-06-17 2017-12-21 セイコーエプソン株式会社 生体情報処理装置、プログラム及び生体情報処理方法
JP2017221551A (ja) * 2016-06-17 2017-12-21 セイコーエプソン株式会社 生体情報処理装置、プログラム、生体情報処理方法、生体情報処理システム及び情報処理装置
EP3316259B1 (fr) * 2016-11-01 2023-11-01 Samsung Electronics Co., Ltd. Procédé de reconnaissance d'activité d'utilisateur et dispositif électronique associé
CN109077710A (zh) * 2017-06-13 2018-12-25 北京顺源开华科技有限公司 自适应心率估计的方法、装置和系统
CN109199327A (zh) * 2017-06-30 2019-01-15 三星电子株式会社 用于连续生物特征监控的自供电可穿戴设备
US10959626B2 (en) 2017-06-30 2021-03-30 Samsung Electronics Co., Ltd. Self-powered wearable for continuous biometrics monitoring
CN113171080A (zh) * 2021-04-19 2021-07-27 中国科学院深圳先进技术研究院 一种基于可穿戴传感信息融合的能量代谢评估方法和系统

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