EP4061215A1 - Torso-mounted accelerometer signal reconstruction - Google Patents
Torso-mounted accelerometer signal reconstructionInfo
- Publication number
- EP4061215A1 EP4061215A1 EP20744177.5A EP20744177A EP4061215A1 EP 4061215 A1 EP4061215 A1 EP 4061215A1 EP 20744177 A EP20744177 A EP 20744177A EP 4061215 A1 EP4061215 A1 EP 4061215A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- motion data
- recorded
- accelerometer
- individual
- torso
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
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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
- A61B5/103—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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6814—Head
- A61B5/6815—Ear
-
- 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
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7278—Artificial waveform generation or derivation, e.g. synthesising signals from measured signals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6823—Trunk, e.g., chest, back, abdomen, hip
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
-
- 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
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
Definitions
- the present disclosure relates to a device and method for torso-mounted accelerometer signal reconstruction.
- Torso-mounted accelerometers are used in sports for motion detection and analysis with aim to improve movement pattern of a wearer of the accelerometer during exercising such as for instance running or cross-country skiing.
- Motion data is recorded and subsequently processed to provide an animation of the wearer’s motion pattern during the exercise for review by the wearer, possibly with feedback to the wearer on improving actions to be taken, such as a proposed change of stride length, an instruction to run in a more upright manner, to more aggressively pivot the arms back and forth, etc.
- Torso-mounted accelerometers tend to sit quite closely to the centre or mass of the wearer’s body, and hence can capture a great deal of motion nuances.
- torso-mounted accelerometers are rather inconvenient as they typically must be fastened and adjusted with a strap over the wearer’s chest and maintained in that position throughout the exercise.
- One object is to solve, or at least mitigate, this problem in the art and thus to provide an improved method of acquiring recorded motion signals of an individual.
- This object is attained in a first aspect by a method of acquiring recorded motion data of an individual.
- the method comprises acquiring motion data of the individual recorded with a first torso-attached accelerometer, acquiring motion data of the individual recorded with a second accelerometer attached to a different part of the individual than the torso, and determining a mapping function configured to map the motion data recorded with the second accelerometer to the motion data recorded with the first accelerometer for subsequently reconstructing torso-recorded motion data from motion data being recorded with the second accelerometer and processed by the determined mapping function.
- a device configured to acquire recorded motion data of an individual.
- the device comprises a processing unit and a memory, said memory containing instructions executable by the processing unit, whereby the device is operative to acquire motion data of the individual recorded with a first torso-attached accelerometer, acquire motion data of the individual recorded with a second accelerometer attached to a different part of the individual than the torso, and to determine a mapping function configured to map the motion data recorded with the second accelerometer to the motion data recorded with the first accelerometer for subsequently reconstructing torso-recorded motion data from motion data being recorded with the second accelerometer and processed by the determined mapping function.
- This object is attained in a third aspect by a method of reconstructing motion data of an individual.
- the method comprises acquiring motion data of the individual recorded with a second accelerometer attached to a different part of the individual than a torso, and processing the acquired motion data in a mapping function having been previously determined by mapping the motion data recorded with the second
- accelerometer (103) to motion data of the individual (too) recorded with a first torso- attached accelerometer (101), thereby reconstructing (S203) torso-recorded motion data.
- a device configured to reconstruct motion data of an individual.
- the device comprises a processing unit and a memory, the memory containing instructions executable by the processing unit, whereby the device is operative to acquire motion data of the individual recorded with a second accelerometer attached to a different part of the individual than a torso, and to process the acquired motion data in a mapping function having been previously determined by mapping the motion data recorded with the second accelerometer to motion data of the individual recorded with a first torso-attached accelerometer, thereby reconstructing torso- recorded motion data.
- the mapping function used is that determined using the method of the first aspect.
- the accelerometer is implemented in for instance a smart watch of in smart phone placed in a holder fastened around an upper part of the wearer’s arm or around the wrist.
- accelerometer signals may be reconstructed from recorded AML accelerometer signals having been processed by said mapping function.
- the reconstructed TM accelerometer signals may be processed in the already available torso-based motion detection and analysis algorithms.
- the mapping function is determined such that an error between the reconstructed torso-recorded motion data and the corresponding motion data recorded with the first accelerometer is minimized.
- a user profile is acquired of the individual for which the motion data is acquired, the user profile being associated with the determined mapping function.
- the user profile comprises information including at least one of weight, height, sex, chest width, placement of the second accelerometer.
- the mapping function is determined based on motion data of a plurality individuals having a similar user profile. [0018] In an embodiment, a mapping function having been previously determined for a first individual is used to reconstruct torso-recorded motion data of a second individual.
- a request to use a determined mapping function is received, the request comprising a user profile of the requesting individual and motion data of the requesting individual recorded with a second accelerometer attached to a different part of the individual than a torso, and the motion data of the requesting individual is processed using a mapping function associated with a user profile best matching the user profile of the requesting individual to reconstruct torso-recorded motion data of the requesting individual.
- a motion pattern of the individual is detected based on the reconstructed torso-recorded motion data.
- Figure 1 illustrates a user wearing a torso-mounted accelerometer
- Figure 2 illustrates a user wearing a head-mounted accelerometer
- Figures 3a-c illustrate motion data recorded in three dimensions X, Y and Z by a torso-mounted accelerometer and a headphone-mounted accelerometer, respectively, according to an embodiment:
- Figure 4 illustrates recording of motion data of a wearer, deriving of a mapping function, and reconstruction of torso-based motion data according to an embodiment
- Figure 5 illustrates recorded and reconstructed accelerometer motion signals according to an embodiment
- Figure 6 illustrates a user requesting to use a previously determined mapping function associated with a user profile matching her own user profile according to an embodiment
- Figure 7 illustrates a device configured to acquire recorded motion data of an individual according to an embodiment.
- a torso-mounted accelerometer 101 has the advantage of being placed close to the centre of mass of the body of the wearer too, and hence can capture a great deal of motion nuances.
- the torso is widely considered to be the best location for placement of an accelerometer and available algorithms utilized for the motion detection and analysis are typically adapted for processing torso-based motion data.
- the torso is not the most convenient position for wearing the accelerometer 101, and a strap 102 is required to fasten the accelerometer 101 over the wearer’s chest.
- a solution is proposed to this problem by allowing placement of the accelerometer elsewhere on the wearer’s body, somewhere more convenient, such as in a holder fastened around an upper part of the wearer’s arm or around the wrist, or in a pair of in-ear headphones or over-ear headphones to be fastened to the wearer’s head. It is also envisaged that the accelerometer is implemented in for instance a smart phone placed in a holder fastened around an upper part of the wearer’s arm or around the wrist.
- Figure 2 illustrates an accelerometer 103 being part of an in-ear headphone attached to the wearer too.
- the accelerometer 103 no longer is placed at the centre of mass of the wearer’s body, which e.g. has the consequence that algorithms developed in this particular technical field no longer will produce reliable motion analysis data as they assume torso-based motion signals for processing, while motion signals originating from other locations of the body will differ from the torso- based motion signals.
- KPIs motion key point indicators
- VO vertical oscillation
- GCT ground contact time
- accelerometer 103 at another mounted location will be adapted and matched to signals of a torso-mounted (TM) accelerometer 101 by utilizing a mapping function, after which process TM accelerometer signals may be reconstructed from recorded AML accelerometer signals having been processed by said mapping function.
- AML torso-mounted accelerometer
- the reconstructed TM accelerometer signals may be processed in the already available torso-based motion detection and analysis algorithms.
- “motion signals” or“motion data” will denote signals produced by the accelerometers 101, 103 from which motion of the wearer too is detected and possibly even reconstructed by means of animation to be presented on a suitable display for review by the wearer.
- TM motion signals will be recorded by a torso-mounted accelerometer 101, while AML motion signals will be recorded by for instance a headphone-mounted accelerometer 103, both being attached to the wearer too.
- Figures 3a-c illustrate accelerometer data recorded in all three dimensions X, Y and Z, respectively, for the TM accelerometer and the AML accelerometer.
- the TM motion signals are illustrated with continuous lines, while the AML motion signals are illustrated with continuous lines. There is typically a non-linear relationship between the TM motion signals and the AML motion signals.
- Figure 4 thus illustrates recording of motion data of the wearer too using the TM accelerometer 101 in step Slot and recording of motion data of the wearer too using the AML accelerometer 103 in step S102 during the derivation phase.
- the AML accelerometer data is adjusted such that it matches the TM
- a user profile of the wearer too may be acquired and associated with the mapping function M is shown in step S104. This will be discussed further hereinbelow.
- a mathematical mapping function M is derived, thereby mapping the two sets of motion data to each other such that during a subsequent TM accelerometer motion data reconstruction phase, AML accelerometer motion data being recorded in step S201 by the AML accelerometer 103 and processed in step S202 by the mapping function M will result in (hypothetically recorded) TM accelerometer motion data being reconstructed:
- TM accelerometer motion data M(AML accelerometer motion data)
- TM accelerometer motion data may subsequently be reconstructed in step S203 from AML accelerometer motion data having been recorded in step S201 and processed by the determined mapping function M in step S202.
- mapping function M can be determined for the wearer too in the derivation phase using motion data of the TM accelerometer 101 and the AML accelerometer 103.
- TM accelerometer motion data can be reconstructed by processing recorded AML accelerometer motion data in the mapping function M.
- the wearer too will only initially - during the derivation phase - have to wear the TM accelerometer 101.
- the signals of the AML accelerometer 103 can be utilized to reconstruct the TM accelerometer signals and the TM accelerometer tot is thus advantageously no longer needed to obtain motion analysis information.
- mapping function M the aim is to find an accurate predictor M which results in the following mapping:
- the TM accelerometer motion signal by creating an estimate f (t) of the TM accelerometer motion signal based on the AML accelerometer motion signal over a time window defined by a lower bound and an upper bound t - di 3 ⁇ 4 and t - 8 Ub , respectively.
- the mapping function M may have to be fine-tuned - i.e. calibrated - such that an error between the estimates f (t) of the TM accelerometer motion signals (produced by processing the AML accelerometer motion signals a(t) with the mapping function M) and the actually measured values r(t) of the TM accelerometer motion signals is minimized.
- the respective accelerometer motion signals r(t) and a(t) are concurrently measured.
- the mapping function M is determined such that the error between the reconstructed value f(t)- i.e. the estimated value - of the TM accelerometer motion signal at a given point in time t, which is based on the AML accelerometer motion signal a(t) processed by the mapping function M, and the measured value r(t) of the TM accelerometer signal at said given point in time is minimized.
- Each one of these approaches represents a rich class of functions that can be utilized to embody spatio-temporal mapping provided by M.
- the selection of the particular approach to be utilized depends on a number of factors, such as for instance desired level of accuracy required for the mapping or computational load required to reconstruct the TM motion data.
- a recurrent neural network such as a so called“long short term memory” network may be used to derive a highly accurate mapping model M, but it may computationally be too demanding for e.g. smart watch deployment; in which case a convolutional neural network may be considered being less accurate but on the other hand requiring less computational power.
- Figure 5 illustrates in an upper view a motion signal recorded by a chest- mounted accelerometer 101 and a motion signal recorded by an ear-mounted
- the motion signal of the chest-mounted accelerometer 101 is illustrated with a continuous line, while the motion signal of the ear-mounted accelerometer 103 is illustrated with a dotted line.
- accelerometer 101 continuously line
- the chest-recorded motion signal reconstructed/estimated from the motion signal recorded by the ear-mounted accelerometer 103 (dotted line) and processed by the mapping function M.
- the mapping function M it is possible to reconstruct a chest-recorded motion signal which is quite accurate as compared to the actual chest-recorded motion signal.
- the accuracy of the reconstruction may be determined by computing a so-called mean-squared error (MSE), wherein the mapping function M may be adjusted such that the reconstructed chest-recorded motion signals corresponds to the actually recorded chest signals in such a manner that the MSE is small (or even minimized).
- MSE mean-squared error
- the wearer no longer needs to use the TM accelerometer but can henceforth use her headphone-mounted accelerometer to record motion data while the algorithms utilized for motion detection and analysis - possibly hosted on a computer or smart-phone to which the AML accelerometer data is supplied after exercise (or in real-time) and on a display of which an animation may be presented - are the available algorithms utilized to process TM motion data.
- the derivation process is performed by one device, such as a cloud server, while the reconstruction process is performed by another, e.g. a smart phone to which the determined mapping function M has been transferred from the cloud server.
- each individual user will undergo the calibration process for best and most accurate end result.
- a generic model M is derived using AML and TM acceleration motion data of one or more users and that future users can adopt the derived model M with good result.
- mapping function M for each determined mapping function M, a corresponding user profile is stored containing information such as weight, height, sex, chest width, etc., wherein a new user can select a function M based on the closest matching user profile.
- TM and AML accelerometer data of a great number of users having an identical or similar user profile is acquired to determine a corresponding mapping function M. That would have the advantage that a great amount of data originating from users having identical, or at least similar, user profiles will be used to derive a mathematical mapping model M.
- a user too having for instance an earphone- mounted accelerometer 103 and a smart phone 104 may download a selected mapping function M from a server 105 located in the cloud 106 to a motion analysis app on the phone 104.
- the user too may selected a mapping function M which best matches her individual user profile.
- the user too may supply her user profile to the cloud server 105 along with motion data recorded by the AML accelerometer 103 for the user too to the cloud server 105, which subsequently reconstructs TM motion data based on the received user profile and the AML accelerometer motion data and possibly returns the reconstructed TM motion data to the smart phone 104 for presentation to the user too.
- a mapping function M based on the recorded TM accelerometer motion data and the recorded AML accelerometer motion data as described in step S103 in Figure 4, the cloud server 105 will also in step 104 acquire a user profile of the user for which the function M is derived.
- a specific user profile may specify:
- AML accelerometer placement ear.
- the user profile is stored at the server 105 as profilei and the corresponding determined mapping function is stored as Ml.
- a second specific user profile profile2 is stored along with the corresponding determined mapping function M2.
- a third specific user profile profiles is stored along with the corresponding determined mapping function M3, and so on.
- the cloud server 105 may contain a great number of determined mapping functions and associated user profiles.
- a user 100 wishing to acquire a suitable mapping function may thus make such a request to the server 105 via her smart phone 104 and include her user profile with the request. Assuming that the user 100 provides identical or similar information as that contained in the above exemplified user profile profilei, the server 105 will return the associated mapping function Ml.
- the server 105 may itself use the mapping function Ml for reconstruction of TM accelerometer motion data given that the user too also provides her AML accelerometer motion data on which the
- the server 105 may thus subsequently supply the smart phone 104 with the reconstructed of TM accelerometer motion data, or possibly even an animation illustrating the motion pattern of the requesting individual too.
- mapping function Ml is likely to be able to accurately reconstruct TM accelerometer motion signals from the motion signals recorded by the ear-mounted accelerometer 103 given that the user too has the same user profile as the user associated with profilei. Thus, there is advantageously no need for the user too to wear a torso-mounted accelerometer to derive a mapping function, but an already determined mapping function Ml can be used with good accuracy.
- Figure 7 illustrates a device 105, e.g. a cloud server, according to an
- the steps of the method performed by the device 105, being embodied e.g. in the form of a computer, server, smart phone, etc., of recording motion data of an individual according to embodiments are in practice performed by a processing unit 120 embodied in the form of one or more microprocessors arranged to execute a computer program 121 downloaded to a suitable storage volatile medium 122 associated with the microprocessor, such as a Random Access Memory (RAM), or a non-volatile storage medium such as a Flash memory or a hard disk drive.
- the processing unit 120 is arranged to cause the device 105 to carry out the method according to embodiments when the appropriate computer program 121 comprising computer-executable instructions is downloaded to the storage medium 122 and executed by the processing unit 120.
- the storage medium 122 may also be a computer program product comprising the computer program 121.
- the computer program 121 may be transferred to the storage medium 122 by means of a suitable computer program product, such as a Digital Versatile Disc (DVD) or a memory stick.
- DVD Digital Versatile Disc
- the computer program 121 may be downloaded to the storage medium 122 over a network.
- the processing unit 120 may alternatively be embodied in the form of a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a complex programmable logic device (CPLD), etc.
- DSP digital signal processor
- ASIC application specific integrated circuit
- FPGA field-programmable gate array
- CPLD complex programmable logic device
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Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
SE1950879A SE1950879A1 (en) | 2019-07-10 | 2019-07-10 | Torso-mounted accelerometer signal reconstruction |
PCT/SE2020/050619 WO2021006790A1 (en) | 2019-07-10 | 2020-06-15 | Torso-mounted accelerometer signal reconstruction |
Publications (1)
Publication Number | Publication Date |
---|---|
EP4061215A1 true EP4061215A1 (en) | 2022-09-28 |
Family
ID=71741868
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP20744177.5A Pending EP4061215A1 (en) | 2019-07-10 | 2020-06-15 | Torso-mounted accelerometer signal reconstruction |
Country Status (3)
Country | Link |
---|---|
EP (1) | EP4061215A1 (en) |
SE (1) | SE1950879A1 (en) |
WO (1) | WO2021006790A1 (en) |
Family Cites Families (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP2636361B1 (en) * | 2012-03-06 | 2022-03-30 | Polar Electro Oy | Exercise monitoring using acceleration measurement |
US11006856B2 (en) * | 2016-05-17 | 2021-05-18 | Harshavardhana Narayana Kikkeri | Method and program product for multi-joint tracking combining embedded sensors and an external sensor |
US20180070864A1 (en) * | 2016-06-02 | 2018-03-15 | Matthew Schuster | Methods and devices for assessing a captured motion |
WO2018022657A1 (en) * | 2016-07-25 | 2018-02-01 | Ctrl-Labs Corporation | System and method for measuring the movements of articulated rigid bodies |
US10646139B2 (en) * | 2016-12-05 | 2020-05-12 | Intel Corporation | Body movement tracking |
WO2019014238A1 (en) * | 2017-07-10 | 2019-01-17 | Georgia Tech Research Corporation | Systems and methods for tracking body movement |
US11266328B2 (en) * | 2017-08-03 | 2022-03-08 | Latella Sports Technologies, LLC | Systems and methods for evaluating body motion |
EP3676818A1 (en) * | 2017-08-29 | 2020-07-08 | Myotest SA | A method and device for retrieving biomechanical parameters of a stride |
-
2019
- 2019-07-10 SE SE1950879A patent/SE1950879A1/en not_active Application Discontinuation
-
2020
- 2020-06-15 EP EP20744177.5A patent/EP4061215A1/en active Pending
- 2020-06-15 WO PCT/SE2020/050619 patent/WO2021006790A1/en active Application Filing
Also Published As
Publication number | Publication date |
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SE1950879A1 (en) | 2021-01-11 |
WO2021006790A1 (en) | 2021-01-14 |
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