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Patent Searching and Data

Document Type and Number:
WIPO Patent Application WO/2018/152390
Kind Code:
A wearable electronic device and/or an App for personalized health and/or training potentially with a connected live response function capable of interacting audible, visible and/or another form of communicating warnings, strength consumption, teaching, suggestions and/or alternative movement patterns and speed on the go as well as capturing performance statistics on footwear and ground surface. Enabling a personalized lower impact level on individual body during regular and performance movements regardless of age, muscles and body structure thereby improving health and athletic performance.

GRAM, Jes, Tougaard (10625 Pinnacle Peak Road, Scottsdale, AZ, 85255, US)
Application Number:
Publication Date:
August 23, 2018
Filing Date:
February 16, 2018
Export Citation:
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GRAM, Jes, Tougaard (10625 Pinnacle Peak Road, Scottsdale, AZ, 85255, US)
International Classes:
G01C22/00; A61B5/11; G16H20/30
Foreign References:
Other References:
Attorney, Agent or Firm:
CHALFIE, Edward (602 Picardy Circle, Northbrook, IL, 60062, US)
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What is claimed is:

1. A method of monitoring the impact level human activity using at least one sensor comprising: monitoring the impact level and/or accelerations using an inertial sensor; identifying a user activity based on the impact level and/or accelerations; making a first estimation of a user activity statistic associated with the user activity based on the impact level and/or accelerations, the user activity statistic being one of: a distance traveled or a speed of travel e.g. supported by GPS; making a second estimation of the user activity statistic based on the impact level during this activity enabling data and/or recommendations to lessen the stress and impact level on your body e.g. on running profile, footwear choice and stride length in historic and/or live format.

2. The method of claim 1, further comprising: transmitting the impact level and/or accelerations and/or the location

information to an external computing device; and receiving an externally calculated user activity statistic potentially

combining the impact data with third party data.

3. The method of claim 2, wherein the accelerations and the location information are transmitted to the external computing device after the user activity ends.

4. The method of claim 1, further comprising: live

transmitting, displaying and/or communicating to the person preforming the activity the impact level and/or accelerations and/or the location information on a continues basis enabling the person to make adjustments and/or corrections to a certain movement pattern.

5. The method of claim 1, further comprising: transmitting the impact level and/or accelerations and/or the location

information to an external computing device; and receiving an externally calculated user activity statistic potentially

combining the impact data with third party data.

6. The method of claim 1, wherein the user activity

statistic is one or more of an impact level, a distance traveled, a route traveled, a speed of travel, a current position, and a periodic human motion count.

7. The method of claim 1, wherein the impact App/device has a calibration mode that can make a personalized profile of the user e.g. by walking and running barefooted on a solid surface to establish a basic personalized impact profile for the user.

8. The method of claim 1, wherein the impact App/device has a can monitor the impact level from the ground surface, flooring combined with the used footwear potentially justifying changes to the working environment and/or working routines.

9. The method of claim 1, wherein the impact App/device interacts with a map service that can recommend low impact travel routes with softer ground surfaces and avoid hard ground surfaces like routes with a lot of asphalt and concrete as much as

possible .

Wearable Personalized Heath and Training Device


The development of Micro-Electro-Mechanical Systems (MEMS) technology has enabled manufacturers to produce inertial sensors (e.g., accelerometers ) that have a small size, cost, and power consumption. Global positioning system (GPS) sensors have also been developed that are of small size, cost and power consumption. These navigation systems can be capable of performing an overall activity identification based on location movement and time. The navigation systems have the limitation of being incapable of determining activity statistics associated with particular

activities .

Recent advances have enabled inertial sensors and GPS sensors to be installed in some mobile commercial electronic devices such as cellular phones and wearable's. But no such devices are

currently offered that perform sensor fusion to combine GPS sensor readings and inertial sensor readings for personalized health and/or training tracking the impact level a persons body.

Numerous tradeoffs are considered in engineering an

electronic device to be worn on the human body. Functionality, battery life, ergonomic comfort, and aesthetics, for example, all come into play. In some cases, the overall rigidity of

traditionally engineered electronic structures is an obstacle to creating a functional, comfo table, and attractive device, Most fitness trackers measure motion and most of today's wearable's come with a 3-axis accelerometer to track movement in every direction, and some come with a gyroscope too to measure orientation and rotation and some can measure the movement speed of the fitness tracker.

The data collected is then converted into steps ana activity and from their into calories and sleep quality based on some predetermined assumptions in the algorithms.

Another tracking feature is the altimeter that can measure your altitude, when working out the height of the mountains you've climbed or the number of flights of stairs you've managed to get up and down during the day. All of this information is collected and crunched to create an overall reading, and the more sensors your tracker has, the more accurate its data.

These sensors measure the acceleration, frequency, duration, intensity and patterns of movement .

All this data can help a tracker determining the scope of activity from a standardized matrix that can be tweaked, into better accuracy by entering height, weight and. age.

Some of the current top wearable fitness tracking devices and their specs are:

The Jawbone UPS is one of the most sensor-packed trackers, squeezing in temperature sensors and a bioimpedance sensor

alongside the familiar accelerometer we've already mentioned.

Bioimpedance sensors check the resistance of your skin to a tiny electric current, and the four electrodes on the inside of the UPS fitness tracker are clearly visible.

Other wearables such as the Fitbit Charge 2 use optical sensors to shine a light on your skin and measure your pulse through it: the light illuminates your capillaries, then a sensor measures the rate at which, your blood is being pumped (and thus your heart rate) . These optical sensors are less effective than bioimpedance as a gauge of your overall health but can be more useful if you want to check your heart rate as you exercise or work out .

When it comes to tracking sleep using a process called actigraphy, your tracker translates wrist movements into sleep patterns as best it can, and as with steps there's some guesswork and estimating involved. It's a useful guide, but it's not as accurate as polysomnograph, this is what the experts use to measure sleep in a lab, and it monitors brain activity rather than how much the wearer is tossing and turning .

It is often difficult to get two fitness trackers to agree on how much activity you've got through in a day or what your heart rate actually is. That's because the sensors inside each device aren't perfect at measuring what you * re doing plus the fact that people come in all shapes and sizes and most of the devices use slightly different algorithms to translate the raw data into actual statistics. Anything from a concrete floor to a plush carpet cam throw off the accuracy of your fitness tracker.

When it comes to calories, an app needs more than just a step count to make the calculation: that's why you'll often be asked for your age, gender, height and weight too. The algorithms used by each manufacturer aren't made publicly available, ana mostly kept as trade secrets they use to get the best and most accurate results, but the more sensors and data points used the more accurate the results are likely to be.

To really tell now many calories you're burning, for example, a tracker needs to add data about your heart rate and how much you're perspiring into the algorithm alongside how many steps you're taking.

One of the most well known tracking platforms out there is MotionX, which you can find in e.g. Nike's running apps.

The apps are the final link in the chain, presenting the data in a user-friendly format once it's been passed through various algorithms and refined accordingly. Most fitness tracking apps come with the ability to add data and exercise manually as well.

Several phones now have the option for Apps like Apple

Health, Google Fit and with the array of sensors inside our phones, developing apps is a lot easier then it used to be, especially with the arrival of Apple's M7 and M8 chips where its all built in so the data is essentially ready to go and. it's up to developers to do something distinctive with it. New activity Apps can develop using the accelerometer and GPS in e.g. the iPhone.

But even with the same hardware platform two apps won't necessarily report the same statistics, even if they're using the same raw data due to different algorithms and lack of accurate personalized movement pattern,


This invention relates to monitoring human activity and the impact level caused on the body from this activity preferable delivering live personalized data based on individual body

structure and movements. And more particularly to help lowering the impact level by recommending changes in length in strides, footwear and ground surface combining this data with accurately calculating user activity statistics using e.g. a sensor for speed, a location based sensor and an inertial sensor, third party data or any combination hereof.


One type of user activity statistic of a motion processor could be to determine the number of steps (or other periodic human motions) taken. In one embodiment, a series of motion criteria are applied to the acceleration measurement data to detect steps and impact level on the activity and e.g. speed or other periodic human motions. In one embodiment, a different set of motion criteria may apply for running, for walking, and/or for other periodic human motions. For example, a first threshold and first cadence window may be used to determine if a step has occurred while a user is running and the impact level of this, and a second threshold and second cadence window may be used to determine if a step has occurred while a user is walking and the impact level of this .

Another type of user activity statistic that can be

determined by the distance traveled. A user's stride length can be determined for a step based on gait characteristics associated with the step. Examples of gait characteristics include step cadence, heel strike, and other gait characteristics that can be derived from acceleration measurements. For example, if a step cadence of a certain number of steps per minute and a specific heel strike are detected, a stride length of a certain length may be determined. Step detection and the calculated stride length of each step can then be used to calculate distance traveled e.g. combined with GPS information.

In one embodiment, the stride length is determined by

comparing gait characteristics to a stride length correlation model. The stride length correlation model correlates stride lengths to steps and impact level based on gait characteristics associated with the step e.g. step cadence, heel strike, and other gait characteristics that can be derived from acceleration

measurements and combined with the impact level. In one

embodiment, the stride length correlation model includes a stride length algorithm that identifies a specific stride length when one or more gait characteristics are used as input based on the impact level and combined with the projected changes in the impact level by increasing or decreasing the length of the stride length.

The stride length algorithm may vary depending on user attributes e.g. depending on user weight, height, athletic

ability, footwear, ground surface, etc. but the common nominator will be a personalized indicator on how to lower the impact level on the body based on the activity preformed.

This will enable a person to determine benefits or lack of so based on a personalized body impact level and instead of relying on some standardized shoe that might have worked a lot better had your body weight or height been different. This invention could help regardless of the impact level was used to lessen the strain on a sore back or improving your performance running a marathon where a lesser impact level would preserve more strength for more consistency and pace.

From a health and life quality prospective the impact data could enable your doctor to give recommendations to lessen the stress and impact level on your body enabling other activity forms then the normally recommended low impact activities like e.g.

bicycling .

From a dedicated training prospective the impact data could enable your coach or the App to give recommendations on running profile, footwear choice and stride length even when running potentially combining the impact data with third party data like e.g. MotionX on the fly advancing the performance level of an athlete by a better energy management.

Improvement are constantly being made in the working

environment space e.g. soft paddings under working stations to lessen the strain on the workers body and by using this impact App/device it will be possible to monitor the strain on a body by getting individual/personalized data that can help improve a persons working environment.

Another benefit to the invention as claimed is that it lend itself in many different configurations e.g. the most basic model where you just register the level of impact from the sensor without taking into account what activity that is being preformed e.g. working or running to the bells and whistles that takes activity, ground surfaces, foot wear and third party input into complex algorithms with sophisticated data output.

One of these sophisticated data output help a person

determine what footwear configuration would give the best impact output for their need of activity based on their body weight and configuration/ figure and movement pattern weather that would be the height and/or shape/angel of the heel and/or the cushion level in different parts of the footwear.

Since this impact App/device foremost measures the impact it automatically adjust itself to the specific user as it is the impact on a given body and not a standardized walking, running, climbing stairs, counting calories algorithm that can not accurately adjust itself to height, length of legs, body mass index and overall fitness of a person.

All people have different body configurations and this along with fitness, muscles, fatigue will determine/impact how good a persons suspension level is at a given time and how a certain activity will impact that persons body.

This invention gives the user personalized information on their personal impact level at different activities enabling them to improve their body health/longevity and/or training pattern.