AMENDED CLAIMS

received by the International Bureau on 03 April 2018 (03.04.2018)

CLAIMS

1. An alert system for communicating an event sensed by a body worn sensor.

2. The system of claim 1 wherein the body worn sensor is mechanically associated with the body.

3. The system of claim 1 or 2 wherein the event is a fall event.

4. The system of any previous claim wherein the sensor includes a processor in communication with memory for on-board processing of at least one signal.

5. The system of any previous claim wherein the sensor includes a timer.

6. The system of any previous claim wherein the sensor includes a GPS device.

7. The system of any previous claim wherein the sensor includes a communications device.

8. The system of any previous claim wherein the communications device includes broadband network interconnectivity for connection to the Internet.

9. The system of any previous claim wherein the communications device includes cellular telephone network interconnectivity for connection of the device to a local cellular telephone network.

10. The system of any previous claim wherein the sensor includes an accelerometer.

11. The system of any previous claim wherein the at least one signal is an acceleration signal.

12. The system of any previous claim wherein the at least one signal is a timing signal.

13. The system of any previous claim wherein the signal is an acceleration signal derived from the accelerometer. 2

14. The system of any previous claim wherein the signal is a timing signal derived from the timer.

15. The system of any previous claim wherein the signal is a GPS signal derived from the GPS device.

16 The system of any previous claim wherein the event is a fall event.

17 The system of any one of claims 1 to 15 wherein the event is a seizure event.

18 The system of any one of claims 1 to 15 wherein the event is a sleepwalk event.

19 The system of any previous claim further including an additional monitoring or sensing device.

20 The system of claim 19 wherein the additional monitoring or sensing device includes at least a speaker and a microphone and is in communication with a web enabled server.

21 The system of claim 20 wherein the web enabled server executes an application whereby functionality of the body worn sensor is supplemented with the functionality of the additional monitoring or sensing device.

22 The system of claim 21 wherein the body worn sensor is mounted to the wrist of a user.

23 The system of any one of claims 1 to 22 wherein an artificial intelligence AI capability is programmed into memory 18 for execution by processor 1 of the body worn sensor.

24 The system of claim 23 wherein an AI program is executed on the processor associated with server located remote from the sensor 14.

25 The system of claim 23 or 24 wherein the AI capability learns from false positive event determination and false negative event determination in order to statistically improve reliability of detection of an event over time and with particular reference to learned attributes of the data associated with any given user 12. 3

26. A fall detection apparatus comprising:

an accelerometer which communicates an acceleration signal to a processor;

the acceleration signal quantifying acceleration on a substantially continuous basis relative to a reference frame;

a timer which communicates a time reference signal to the processor;

the processor monitoring the acceleration signal on a substantially continuous basis;

the processor monitoring the timing signal on a substantially continuous basis;

and whereby if the acceleration signal is within a first low acceleration range for a predetermined period of time and is followed by a second high acceleration signal in a second predetermined period of time a fall condition is determined by the processor.

27. The fall detection apparatus of claim 26 wherein the processor monitors the timing signal and the acceleration signal during a third predetermined period of time subsequent to the second predetermined period of time whereby if the acceleration signal remains in a predetermined very low range during the third predetermined period of time then it is determined that the user is immobile and a fall detection event is confirmed.

28. The fall detection apparatus of claim 26 or claim 27 wherein when a fall condition is determined by the processor a fall signal is transmitted to a remote location.

29. The fall detection apparatus of claim 26 or 27 or 28 wherein when a fall condition is determined by the processor then a fall signal is communicated locally.

30. The fall detection apparatus of claim 26, 27, 28 or 29 wherein the acceleration signal is referenced against a reference frame.

31. The fall detection apparatus of claim 30 wherein the reference frame is the surface upon which a user of the fall detection apparatus is supported.

32 The fall detection apparatus of any one of claims 26 to 31 wherein the fall detection apparatus is a wrist mounted fall detection apparatus. 4

33. A detection and communication system which reads vital signs of the body of a user utilising a sensing device and applies algorithms to interpret the vital signs and then send a notification with an escalation process to nominated carers if the user is interpreted as having a fall or fit or seizure.

34. The system of claim 33 wherein the device is a smartwatch or smart phone (for example utilising the iOS, Android or Pebble operating systems).

35. The system of claim 33 or 34 wherein doctors or other parties can log in to a secured dashboard and check user data in real time.

36. The system of any one of claims 33 to 36 wherein doctors or other parties can analyse the history of the user.

37. The system of any one of claims 33 to 36 wherein users/patients can also utilise user data derived by the system to keep track of fall or fit or seizure episodes and monitor their progress.

38 A seizure detection apparatus comprising:

an accelerometer which communicates an acceleration signal to a processor;

the acceleration signal quantifying acceleration on a substantially continuous basis relative to a reference frame;

a timer which communicates a time reference signal to the processor;

the processor monitoring the acceleration signal on a substantially continuous basis;

the processor monitoring the timing signal on a substantially continuous basis;

and whereby if the acceleration signal oscillates within a predetermined range for a predetermined period of time then a seizure event is determined and signalled.

39 The seizure detection apparatus of claim 38 wherein the seizure detection apparatus is wrist mounted seizure detection apparatus.

40 A sleepwalk detection apparatus comprising:

an accelerometer which communicates an acceleration signal to a processor;

the acceleration signal quantifying acceleration on a substantially continuous basis relative to a reference frame; 5

a timer which communicates a time reference signal to the processor;

the processor monitoring the acceleration signal on a substantially continuous basis;

the processor monitoring the timing signal on a substantially continuous basis;

and whereby if the acceleration signal indicates a walking movement during a predetermined period of time which exceeds a minimum walking time and which is determined to be a bed time of the user then a sleepwalk event is deteimined and signalled.

41 The sleepwalk detection apparatus of claim 40 wherein the sleepwalk detection apparatus is wrist mounted sleepwalk detection apparatus.

42 A method of detecting a fall event comprising:

providing an accelerometer which communicates an acceleration signal to a processor;

the acceleration signal quantifying acceleration on a substantially continuous basis relative to a reference frame;

providing a timer which communicates a time reference signal to the processor;

the processor monitoring the acceleration signal on a substantially continuous basis;

the processor monitoring the timing signal on a substantially continuous basis;

and whereby if the acceleration signal is within a first low acceleration range for a predetermined period of time and is followed by a second high acceleration signal in a second predetermined period of time a fall condition is determined by the processor.

43 A method of seizure detection comprising:

providing an accelerometer which communicates an acceleration signal to a processor;

the acceleration signal quantifying acceleration on a substantially continuous basis relative to a reference frame;

providing a timer which communicates a time reference signal to the processor;

the processor monitoring the acceleration signal on a substantially continuous basis;

the processor monitoring the timing signal on a substantially continuous basis;

and whereby if the acceleration signal oscillates within a predetermined range for a predetermined period of time then a seizure event is determined and signalled.

44 A method of detecting a sleepwalk event comprising:

providing an accelerometer which communicates an acceleration signal to a processor; 6

the acceleration signal quantifying acceleration on a substantially continuous basis relative to a reference frame;

providing a timer which communicates a time reference signal to the processor;

the processor monitoring the acceleration signal on a substantially continuous basis;

the processor monitoring the timing signal on a substantially continuous basis;

and whereby if the acceleration signal indicates a walking movement during a predetermined period of time which exceeds a minimum walking time and which is determined to be a bed time of the user then a sleepwalk event is determined and signalled.

45. A fall detection apparatus comprising:

an accelerometer which communicates an acceleration signal to a processor;

the acceleration signal quantifying acceleration on a substantially continuous basis relative to a reference frame;

a timer which communicates a time reference signal to the processor;

the processor monitoring the acceleration signal on a substantially continuous basis;

the processor monitoring the timing signal on a substantially continuous basis;

and whereby if the acceleration signal is within a first low acceleration range for a predetermined period of time and is followed by a second high acceleration signal in a second predetemiined period of time a fall condition is determined by the processor; and wherein the processor monitors the timing signal and the acceleration signal during a third predetemiined period of time subsequent to the second predetermined period of time whereby if the acceleration signal remains in a predetermined very low range during the third predetermined period of time then it is determined that the user is immobile and a fall detection event is confirmed.

46. The fall detection apparatus of claim 45 wherein when a fall condition is determined by the processor a fall signal is transmitted to a remote location.

47. The fall detection apparatus of claims 45 or 46 wherein when a fall condition is determined by the processor then a fall signal is communicated locally. 7

48. The fall detection apparatus of claims 45, 46 or 47 wherein the acceleration signal is referenced against a reference frame.

49. The fall detection apparatus of claims 48 wherein the reference frame is the surface upon which a user of the fall detection apparatus is supported.

50 The fall detection apparatus of any one of claims 45 to 49 wherein the fall detection apparatus is a wrist mounted fall detection apparatus.

51 The apparatus of any one of claims 45 to 50 wherein an artificial intelligence AI capability is programmed into memory 18 for execution by processor 1 of the body worn sensor.

52 The apparatus of claim 51 wherein an AI program is executed on the processor associated with server located remote from the sensor 14.

53 The system of claim 51 or 52 wherein the AI capability learns from false positive event determination and false negative event determination in order to statistically improve reliability of detection of an event over time and with particular reference to learned attributes of the data associated with any given user 12.

54 A method of detecting a fall event comprising:

providing an accelerometer which communicates an acceleration signal to a processor;

the acceleration signal quantifying acceleration on a substantially continuous basis relative to a reference frame;

providing a timer which communicates a time reference signal to the processor;

the processor monitoring the acceleration signal on a substantially continuous basis;

the processor monitoring the timing signal on a substantially continuous basis;

and whereby if the acceleration signal is within a first low acceleration range for a predetemiined period of time and is followed by a second high acceleration signal in a second predetermined period of time a fall condition is determined by the processor; and wherein the processor monitors the timing signal and the acceleration signal during a third predetermined period of time subsequent to the second predetermined period of time whereby if the acceleration signal remains in a 8

predetermined very low range during the third predetermined period of time then it is determined that the user is immobile and a fall detection event is confirmed.

55 The mothed of claim 54 wherein an artificial intelligence AI capability is programmed into memory 18 for execution by processor 1 of the body worn sensor.

56 The mothed of claim 55 wherein an AI program is executed on the processor associated with server located remote from the sensor 14.

57 The mothed of any one of claims 55 or 56 wherein the AI capability learns from false positive event determination and false negative event determination in order to statistically improve reliability of detection of an event over time and with particular reference to learned attributes of the data associated with any given user 12.

58. A detection and communication system which reads vital signs of the body of a user utilising a sensing device in a form of a body worn sensor and applies algorithms to interpret the vital signs and then send a notification with an escalation process to nominated carers by way of a server incorporating a processor if the user is interpreted as having a fall or fit or seizure; said system implemented by means of a local processor associated with the body worn sensor and a separate processor associated with the server.

59 The system of claim 58 wherein an artificial intelligence AI capability is programmed into memory 18 for execution by processor 1 of the body worn sensor.

60 The system of claim 59 wherein an AI program is executed on the processor associated with server located remote from the sensor 14.

61 The system of claim 59 or 60 wherein the AI capability learns from false positive event determination and false negative event determination in order to statistically improve reliability of detection of an event over time and with particular reference to learned attributes of the data associated with any given user 12.

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62. The system of any one of claims 58 to 61 wherein the device is a smartwatch or smart phone (for example utilising the iOS, Android or Pebble operating systems).

63. The system of any one of claims 58 to 62 wherein doctors or other parties can log in to a secured dashboard and check user data in real time.

64. The system of any one of claims 58 to 63 wherein doctors or other parties can analyse the history of the user.

65. The system of any one of claims 58 to 64 wherein users/patients can also utilise user data derived by the system to keep track of fall or fit or seizure episodes and monitor their progress.