Wearable device

• US 20070161874A1
• Filed: 06/24/2005
• Published: 07/12/2007
• Est. Priority Date: 07/07/2004
• Status: Active Grant

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Aerts, Steven
• Granted Patent: US 8,021,297 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/222 combined with detection or measurement of physiological parameters, e.g. heart rate
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface

Abstract

A wearable device comprising at least two measuring devices (2,3,4,5,6) providing primary data, a processor (1) designed for calculating secondary data from the primary data, for calculating reference data from the primary and/or the secondary data and for comparing the reference data with at least two specific data pattern to automatically recognize an activity, and a memory (7) designed for storing the primary, the secondary data and the at least two specific data pattern each assigned to a certain activity. The activity recognition is performed by loading the primary and/or the secondary data to the processor (1), calculating the reference data, comparing the reference data with the specific data patterns, assigning the primary and/or the secondary data with the recognized activity and storing the assigned primary and/or secondary data in the memory (7).

Claims

1. A wearable device comprising

at least two measuring devices (2,3,4,5,6) providing primary data,

a processor (1) designed for calculating secondary data from the primary data and for calculating reference data from the primary and/or secondary data and for comparing the reference data with at least two specific data patterns to automatically recognize an activity, and

a memory (7) designed for storing the primary data, the secondary data and the at least two specific data pattern each assigned to a certain activity.

2. A wearable device according to claim 1, wherein the primary and/or the secondary data correspond to at least two of following data classes comprising heart rate, respiration, heat flux, body-temperature, bioelectric impedance, oxygen intake, calorie expenditure, step frequency, GPS signals, environmental temperature, vertical and horizontal acceleration, time, time intervals, velocity and humidity.

3. A wearable device according to claim 1, wherein the specific data pattern assigned to jogging comprising a heart rate with minor variations over long time intervals, a velocity varying between 7 km/h and 25 km/h, a periodic vertical and a minor horizontal acceleration and a constant step frequency for jogging,

4. A wearable device according to claim 1, wherein the specific data pattern assigned to cycling comprising a heart rate mainly constant within medium time intervals, a velocity varying between 10 km/h and 90 km/h, a discrete horizontal and a negligible vertical accelerations and no step frequency.

5. A wearable device according to claim 1, wherein the specific data pattern assigned to water sports comprising a heart rate with minor variations over medium time intervals, a velocity below 20 km/h, low and periodic accelerations, a high skin humidity and no step frequency.

6. A wearable device according to claim 1, wherein the specific data pattern assigned to ball games comprising a strongly varying heart rate, a velocity strongly varying between 0 km/h and 30 km/h, a strongly varying horizontal and vertical acceleration during short time intervals, a strongly varying step frequency.

7. A wearable device according to claim 1, wherein the specific data pattern assigned to fitness sports comprising a heart rate with small variations within medium time intervals, a velocity below 5 km/h, a periodically varying horizontal and vertical acceleration during short time intervals and a minor varying step frequency during short time intervals.

8. A wearable device according to claim 1, wherein the specific data pattern comprises desired upper and lower limits for at least one data class.

9. A wearable device according to claim 1, further comprising a user interface (8) designed for requesting and receiving the assignment of not automatically recognized activities by an user.

10. A wearable device according to claim 9, wherein the processor (1) is designed for calculating new specific data pattern corresponding to activities assigned by the user.

11. A wearable device according to claim 9, wherein the user interface (8) is designed for inserting a re-assignment of an automatically recognized activity by an user and the processor (1) is designed for re-assigning the corresponding primary and/or secondary data.

12. A wearable device according to claim 1, further comprising a computer interface (9) to exchange or add data and/or specific data pattern assigned to certain activities.

13. Activity recognition method for a wearable device according to claim 1, comprising

measuring the primary data as a function of time,

storing the primary and/or the secondary data as a function of time,

loading the primary and/or the secondary data and the specific data patterns,

calculating reference data from the primary and/or secondary data,

comparing the reference data with the specific data patterns to automatically recognize an activity,

assigning the primary and/or the secondary data with the recognized activity

storing the assigned data,

analysing the assigned data, and

adapting the specific data pattern according to the analyzed assigned data.

14. Activity recognition method according to claim 13, further comprising calculation of secondary data from the primary data.

15. Activity recognition method according to claim 13, further comprising

sending a request to a user to assign a not recognized activity,

receiving an user-assignment,

assigning the primary and/or secondary data with the user-assignment,

calculating a new data pattern for the user-assigned activity,

storing the user-assigned data and the new data pattern specific.

16. Activity recognition method according to claim 13, wherein the automatic recognition is interrupted for a user-defined period comprising the following steps

starting the user-defined period via the user interface,

inserting an assignment of the currently performed activity by an user,

assigning all further primary and/or secondary data with the user-assignment,

storing the user-assigned data,

leaving the user-defined period via the user interface,

loading of user-assigned data from the user-defined period,

calculating of new specific data pattern, and

storing the new specific data pattern.