1. A method comprising, by one or more computing devices:
accessing one or more data streams from a plurality of sensors, the sensors comprising one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer, the data streams comprising one or more of heart-rate data of a person from the heart-rate monitor, blood-pressure data of the person from the blood-pressure monitor, pulse oximetry data of the person from the pulse oximeter, mood data of the person from the mood sensor, or accelerometer data of the person from the accelerometer;
accessing a stress model, the stress model comprising baseline renal-Doppler data and two or more of baseline heart-rate data, baseline blood-pressure data, baseline pulse oximetry data, or baseline mood data;
analyzing the data streams with respect to the stress model; and
determining based on the analysis a current stress index of the person.
2. The method of claim 1, wherein one or more of the sensors is affixed to the person’s body.
3. The method of claim 1, wherein the baseline renal-Doppler data is renal-blood-velocity data.
4. The method of claim 1, wherein the baseline renal-Doppler data is renal-blood-flow data.
5. The method of claim 1, wherein the stress model is a stress model of the person and comprises baseline data of the person.
6. The method of claim 1, wherein the stress model is a stress model of a second person and comprises baseline data of the second person, wherein the person and the second person are in the same subset of patients.
7. The method of claim 1, wherein the stress model is a stress model of a subset of patients and comprises baseline data of the subset of patients, wherein the person and the subset of patients share particular distinguishing characteristics.
8. The method of claim 1, wherein the stress model correlates a stress index of the person with the heart-rate data of the person.
9. The method of claim 1, wherein the stress model correlates a stress index of the person with the blood-pressure data of the person
10. The method of claim 1, wherein the stress model correlates a stress index of the person with the pulse oximetry data of the person.
11. The method of claim 1, wherein the stress model correlates a stress index of the person with the mood data of the person.
12. The method of claim 1, wherein the stress model comprises an algorithm that comprises a plurality of variables based on two or more of the heart-rate data of the person, the blood-pressure data of the person, the pulse oximetry data of the person, or the mood data of the person.
13. The method of claim 1, wherein the baseline mood data of the person is used to validate the stress model of the person.
14. The method of claim 1, wherein the baseline mood data of the person is used to identify correlations between a stress index of a person with one or more of the baseline renal-Doppler data, the baseline heart-rate data of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person.
15. The method of claim 1, wherein:
a first set data from the data streams was collected from the person when the person is exposed to a particular stressor; and
a second set of data from the data streams was collected from the person when the person is not exposed to the particular stressor.
16. The method of claim 1, wherein:
a first set data from the data streams was collected from the person when the person is substantially stressed; and
a second set of data from the data streams was collected from the person when the person is substantially unstressed.
17. The method of claim 1, wherein:
a first set data from the data streams was collected from the person when the person is engaged in a first activity; and
a second set of data from the data streams was collected from the person when the person is engaged in a second activity.
18. The method of claim 1, wherein:
the plurality of sensors further comprise a behavioral sensor; and
the data streams further comprise behavioral data of the person from the behavioral sensor.
19. The method of claim 1, wherein:
the plurality of sensors further comprise an electrocardiograph; and
the data streams further comprise electrocardiograph data of the person from the electrocardiograph.
20. The method of claim 1, wherein:
the plurality of sensors further comprise a glucocorticoid meter; and
the data streams further comprise glucocorticoid data of the person from the glucocorticoid meter.
21. The method of claim 1, wherein:
the plurality of sensors further comprise an electromyograph; and
the data streams further comprise electromyograph data of the person from the electromyograph.
22. The method of claim 1, wherein:
the plurality of sensors further comprise a respiration sensor; and
the data streams further comprise respiration data of the person from the respiration sensor.
23. The method of claim 1, wherein:
the plurality of sensors further comprise a galvanic-skin-response sensor; and
the data streams further comprise galvanic-skin-response data of the person from the galvanic-skin-response sensor.
24. The method of claim 1, wherein determining based on the analysis the stress index of the person comprises determining based on the analysis whether the data streams indicate a change in the person’s stress index.
25. The method of claim 1, further comprising:
accessing a prior stress index of the person that precedes the current stress index of the person;
analyzing the current stress index and prior stress index of the person with respect to each other; and
determining whether there is a change in the stress index of the person based on the analysis of the current stress index and the prior stress index with respect to each other.
26. An apparatus comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
access one or more data streams from a plurality of sensors, the sensors comprising one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer, the data streams comprising one or more of heart-rate data of a person from the heart-rate monitor, blood-pressure data of the person from the blood-pressure monitor, pulse oximetry data of the person from the pulse oximeter, mood data of the person from the mood sensor, or accelerometer data of the person from the accelerometer;
access a stress model, the stress model comprising baseline renal-Doppler data and two or more of baseline heart-rate data, baseline blood-pressure data, baseline pulse oximetry data, or baseline mood data;
analyze the data streams with respect to the stress model; and
determine based on the analysis a current stress index of the person.
27. The apparatus of claim 26, wherein one or more of the sensors is affixed to the person’s body.
28. The apparatus of claim 26, wherein the baseline renal-Doppler data is renal-blood-velocity data.
29. The apparatus of claim 26, wherein the baseline renal-Doppler data is renal-blood-flow data.
30. The apparatus of claim 26, wherein the stress model is a stress model of the person and comprises baseline data of the person.
31. The apparatus of claim 26, wherein the stress model is a stress model of a second person and comprises baseline data of the second person, wherein the person and the second person are in the same subset of patients.
32. The apparatus of claim 26, wherein the stress model is a stress model of a subset of patients and comprises baseline data of the subset of patients, wherein the person and the subset of patients share particular distinguishing characteristics.
33. The apparatus of claim 26, wherein the stress model correlates a stress index of the person with the heart-rate data of the person.
34. The apparatus of claim 26, wherein the stress model correlates a stress index of the person with the blood-pressure data of the person
35. The apparatus of claim 26, wherein the stress model correlates a stress index of the person with the pulse oximetry data of the person.
36. The apparatus of claim 26, wherein the stress model correlates a stress index of the person with the mood data of the person.
37. The apparatus of claim 26, wherein the stress model comprises an algorithm that comprises a plurality of variables based on two or more of the heart-rate data of the person, the blood-pressure data of the person, the pulse oximetry data of the person, or the mood data of the person.
38. The apparatus of claim 26, wherein the baseline mood data of the person is used to validate the stress model of the person.
39. The apparatus of claim 26, wherein the baseline mood data of the person is used to identify correlations between a stress index of a person with one or more of the baseline renal-Doppler data, the baseline heart-rate data of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person.
40. The apparatus of claim 26, wherein:
a first set data from the data streams was collected from the person when the person is exposed to a particular stressor; and
a second set of data from the data streams was collected from the person when the person is not exposed to the particular stressor.
41. The apparatus of claim 26, wherein:
a first set data from the data streams was collected from the person when the person is substantially stressed; and
a second set of data from the data streams was collected from the person when the person is substantially unstressed.
42. The apparatus of claim 26, wherein:
a first set data from the data streams was collected from the person when the person is engaged in a first activity; and
a second set of data from the data streams was collected from the person when the person is engaged in a second activity.
43. The apparatus of claim 26, wherein:
the plurality of sensors further comprise a behavioral sensor; and
the data streams further comprise behavioral data of the person from the behavioral sensor.
44. The apparatus of claim 26, wherein:
the plurality of sensors further comprise an electrocardiograph; and
the data streams further comprise electrocardiograph data of the person from the electrocardiograph.
45. The apparatus of claim 26, wherein:
the plurality of sensors further comprise a glucocorticoid meter; and
the data streams further comprise glucocorticoid data of the person from the glucocorticoid meter.
46. The apparatus of claim 26, wherein:
the plurality of sensors further comprise an electromyograph; and
the data streams further comprise electromyograph data of the person from the electromyograph.
47. The apparatus of claim 26, wherein:
the plurality of sensors further comprise a respiration sensor; and
the data streams further comprise respiration data of the person from the respiration sensor.
48. The apparatus of claim 26, wherein:
the plurality of sensors further comprise a galvanic-skin-response sensor; and
the data streams further comprise galvanic-skin-response data of the person from the galvanic-skin-response sensor.
49. The apparatus of claim 26, wherein to determine based on the analysis the stress index of the person comprises to determine based on the analysis whether the data streams indicate a change in the person’s stress index.
50. The apparatus of claim 26, the apparatus further operable when executing instructions to:
access a prior stress index of the person that precedes the current stress index of the person;
analyze the current stress index and prior stress index of the person with respect to each other; and
determine whether there is a change in the stress index of the person based on the analysis of the current stress index and the prior stress index with respect to each other.
51. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
access one or more data streams from a plurality of sensors, the sensors comprising one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer, the data streams comprising one or more of heart-rate data of a person from the heart-rate monitor, blood-pressure data of the person from the blood-pressure monitor, pulse oximetry data of the person from the pulse oximeter, mood data of the person from the mood sensor, or accelerometer data of the person from the accelerometer;
access a stress model, the stress model comprising baseline renal-Doppler data and two or more of baseline heart-rate data, baseline blood-pressure data, baseline pulse oximetry data, or baseline mood data;
analyze the data streams with respect to the stress model; and
determine based on the analysis a current stress index of the person.
52. The media of claim 51, wherein one or more of the sensors is affixed to the person’s body.
53. The media of claim 51, wherein the baseline renal-Doppler data is renal-blood-velocity data.
54. The media of claim 51, wherein the baseline renal-Doppler data is renal-blood-flow data.
55. The media of claim 51, wherein the stress model is a stress model of the person and comprises baseline data of the person.
56. The media of claim 51, wherein the stress model is a stress model of a second person and comprises baseline data of the second person, wherein the person and the second person are in the same subset of patients.
57. The media of claim 51, wherein the stress model is a stress model of a subset of patients and comprises baseline data of the subset of patients, wherein the person and the subset of patients share particular distinguishing characteristics.
58. The media of claim 51, wherein the stress model correlates a stress index of the person with the heart-rate data of the person.
59. The media of claim 51, wherein the stress model correlates a stress index of the person with the blood-pressure data of the person
60. The media of claim 51, wherein the stress model correlates a stress index of the person with the pulse oximetry data of the person.
61. The media of claim 51, wherein the stress model correlates a stress index of the person with the mood data of the person.
62. The media of claim 51, wherein the stress model comprises an algorithm that comprises a plurality of variables based on two or more of the heart-rate data of the person, the blood-pressure data of the person, the pulse oximetry data of the person, or the mood data of the person.
63. The media of claim 51, wherein the baseline mood data of the person is used to validate the stress model of the person.
64. The media of claim 51, wherein the baseline mood data of the person is used to identify correlations between a stress index of a person with one or more of the baseline renal-Doppler data, the baseline heart-rate data of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person.
65. The media of claim 51, wherein:
a first set data from the data streams was collected from the person when the person is exposed to a particular stressor; and
a second set of data from the data streams was collected from the person when the person is not exposed to the particular stressor.
66. The media of claim 51, wherein:
a first set data from the data streams was collected from the person when the person is substantially stressed; and
a second set of data from the data streams was collected from the person when the person is substantially unstressed.
67. The media of claim 51, wherein:
a first set data from the data streams was collected from the person when the person is engaged in a first activity; and
a second set of data from the data streams was collected from the person when the person is engaged in a second activity.
68. The media of claim 51, wherein:
the plurality of sensors further comprise a behavioral sensor; and
the data streams further comprise behavioral data of the person from the behavioral sensor.
69. The media of claim 51, wherein:
the plurality of sensors further comprise an electrocardiograph; and
the data streams further comprise electrocardiograph data of the person from the electrocardiograph.
70. The media of claim 51, wherein:
the plurality of sensors further comprise a glucocorticoid meter; and
the data streams further comprise glucocorticoid data of the person from the glucocorticoid meter.
71. The media of claim 51, wherein:
the plurality of sensors further comprise an electromyograph; and
the data streams further comprise electromyograph data of the person from the electromyograph.
72. The media of claim 51, wherein:
the plurality of sensors further comprise a respiration sensor; and
the data streams further comprise respiration data of the person from the respiration sensor.
73. The media of claim 51, wherein:
the plurality of sensors further comprise a galvanic-skin-response sensor; and
the data streams further comprise galvanic-skin-response data of the person from the galvanic-skin-response sensor.
74. The media of claim 51, wherein to determine based on the analysis the stress index of the person comprises to determine based on the analysis whether the data streams indicate a change in the person’s stress index.
75. The media of claim 51, the media embodying instructions that are further operable when executed to:
access a prior stress index of the person that precedes the current stress index of the person;
analyze the current stress index and prior stress index of the person with respect to each other; and
determine whether there is a change in the stress index of the person based on the analysis of the current stress index and the prior stress index with respect to each other.
76. A system comprising:
means for accessing one or more data streams from a plurality of sensors, the sensors comprising one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, a mood sensor, or an accelerometer, the data streams comprising one or more of heart-rate data of a person from the heart-rate monitor, blood-pressure data of the person from the blood-pressure monitor, pulse oximetry data of the person from the pulse oximeter, mood data of the person from the mood sensor, or accelerometer data of the person from the accelerometer;
means for accessing a stress model, the stress model comprising baseline renal-Doppler data and two or more of baseline heart-rate data, baseline blood-pressure data, baseline pulse oximetry data, or baseline mood data;
means for analyzing the data streams with respect to the stress model; and
means for determining based on the analysis a current stress index of the person.
77. The system of claim 76, wherein one or more of the sensors is affixed to the person’s body.
78. The system of claim 76, wherein the baseline renal-Doppler data is renal-blood-velocity data.
79. The system of claim 76, wherein the baseline renal-Doppler data is renal-blood-flow data.
80. The system of claim 76, wherein the stress model is a stress model of the person and comprises baseline data of the person.
81. The system of claim 76, wherein the stress model is a stress model of a second person and comprises baseline data of the second person, wherein the person and the second person are in the same subset of patients.
82. The system of claim 76, wherein the stress model is a stress model of a subset of patients and comprises baseline data of the subset of patients, wherein the person and the subset of patients share particular distinguishing characteristics.
83. The system of claim 76, wherein the stress model correlates a stress index of the person with the heart-rate data of the person.
84. The system of claim 76, wherein the stress model correlates a stress index of the person with the blood-pressure data of the person
85. The system of claim 76, wherein the stress model correlates a stress index of the person with the pulse oximetry data of the person.
86. The system of claim 76, wherein the stress model correlates a stress index of the person with the mood data of the person.
87. The system of claim 76, wherein the stress model comprises an algorithm that comprises a plurality of variables based on two or more of the heart-rate data of the person, the blood-pressure data of the person, the pulse oximetry data of the person, or the mood data of the person.
88. The system of claim 76, wherein the baseline mood data of the person is used to validate the stress model of the person.
89. The system of claim 76, wherein the baseline mood data of the person is used to identify correlations between a stress index of a person with one or more of the baseline renal-Doppler data, the baseline heart-rate data of the person, the baseline blood-pressure data of the person, or the baseline pulse oximetry data of the person.
90. The system of claim 76, wherein:
a first set data from the data streams was collected from the person when the person is exposed to a particular stressor; and
a second set of data from the data streams was collected from the person when the person is not exposed to the particular stressor.
91. The system of claim 76, wherein:
a first set data from the data streams was collected from the person when the person is substantially stressed; and
a second set of data from the data streams was collected from the person when the person is substantially unstressed.
92. The system of claim 76, wherein:
a first set data from the data streams was collected from the person when the person is engaged in a first activity; and
a second set of data from the data streams was collected from the person when the person is engaged in a second activity.
93. The system of claim 76, wherein:
the plurality of sensors further comprise a behavioral sensor; and
the data streams further comprise behavioral data of the person from the behavioral sensor.
94. The system of claim 76, wherein:
the plurality of sensors further comprise an electrocardiograph; and
the data streams further comprise electrocardiograph data of the person from the electrocardiograph.
95. The system of claim 76, wherein:
the plurality of sensors further comprise a glucocorticoid meter; and
the data streams further comprise glucocorticoid data of the person from the glucocorticoid meter.
96. The system of claim 76, wherein:
the plurality of sensors further comprise an electromyograph; and
the data streams further comprise electromyograph data of the person from the electromyograph.
97. The system of claim 76, wherein:
the plurality of sensors further comprise a respiration sensor; and
the data streams further comprise respiration data of the person from the respiration sensor.
98. The system of claim 76, wherein:
the plurality of sensors further comprise a galvanic-skin-response sensor; and
the data streams further comprise galvanic-skin-response data of the person from the galvanic-skin-response sensor.
99. The system of claim 76, wherein the means for determining based on the analysis the stress index of the person comprises means for determining based on the analysis whether the data streams indicate a change in the person’s stress index.
100. The system of claim 76, further comprising:
means for accessing a prior stress index of the person that precedes the current stress index of the person;
means for analyzing the current stress index and prior stress index of the person with respect to each other; and
means for determining whether there is a change in the stress index of the person based on the analysis of the current stress index and the prior stress index with respect to each other.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A semiconductor integrated circuit device comprising:
a memory cell array for which a data write operation is to be performed by an external write enable signal;
an address transition detecting circuit which detects transition of a column address signal, the column address signal being used to specify a column address of the memory cell array;
a control circuit having a timeout circuit, the control circuit which generates an internal circuit control signal of desired length used to control column access to the memory cell array based on a result of detection by the address transition detecting circuit; and
a column selection line whose selection time is controlled by the control circuit,
wherein the column address signal used for selection of the column selection line is latched without clock-operating the external write enable signal in a period of time in which the column selection line is selected at a write operation time, and
wherein a time for fetching data to be written at the write operation time is determined only by a transition time of the column address signal.
2. A semiconductor integrated circuit device comprising:
a memory cell array;
a first address transition detecting circuit which detects at least one of transition of a chip enable signal, transition of a row address signal and a column address signal and transition of a write enable signal, the chip enable signal being used to specify start of the operation of the memory cell array, the row address signal being used to specify a row address of the memory cell array, the column address signal being used to specify a column address of the memory cell array, the write enable signal being used to specify a write operation of the memory cell array;
a first control circuit having a timeout circuit, the first control circuit which generates an internal circuit control signal of desired length used to control row access to the memory cell array based on a result of detection by the first address transition detecting circuit;
a second address transition detecting circuit which detects only transition of the column address signal;
a second control circuit which controls column access to the memory cell array based on a result of detection by the second address transition detecting circuit;
a column selection line whose selection time is controlled by the second control circuit; and
a mode determination circuit which determines start of a column access mode to generate a mode determination signal and set a column access operation when transition of the column address signal is detected by the second address transition detecting circuit in a case where a condition that the column access operation of the memory cell array is acceptable and then determines an end of the column access mode and sets a standby state in a case where transition of one of the row address and a predetermined address is detected,
wherein the column address signal used for selection of the column selection line is latched in a period of time in which the column selection line is selected when the mode determination circuit determines the column access mode at a write operation time.
3. The semiconductor integrated circuit device according to claim 1, further comprising
a buffer circuit which is supplied with the column address signal and power supply voltage, and
a latch circuit which latches an output signal of the buffer circuit.
4. The semiconductor integrated circuit device according to claim 3, wherein the buffer circuit is controlled to be set into an activenon-active state according to supplynon-supply of the power supply voltage.
5. The semiconductor integrated circuit device according to claim 3, further comprising
a switch which is provided between the buffer circuit and the latch circuit and whose conduction state is controlled by a first control signal,
wherein a connection between the buffer circuit and the latch circuit is controlled according to the conduction state of the switch.
6. The semiconductor integrated circuit device according to claim 5, wherein the switch is set into a non-conductive state to disconnect the buffer circuit and the latch circuit from each other when the address transition detecting circuit detects transition of the column address signal.
7. The semiconductor integrated circuit device according to claim 3, wherein the latch circuit includes a first latch which latches the output signal of the buffer circuit in response to a second control signal, and
a second latch which latches an output signal of the first latch in response to a third control signal and generates a complementary signal.
8. The semiconductor integrated circuit device according to claim 7, wherein the address transition detecting circuit detects transition of the column address signal based on the output signal of the first latch.
9. The semiconductor integrated circuit device according to claim 2, wherein the condition that the column access operation of the memory cell array is acceptable is set based on a sense amplifier enable signal, the sense amplifier enable signal is output from a sense amplifier control circuit which is controlled by the first control circuit.
10. The semiconductor integrated circuit device according to claim 1, wherein the memory cell array is configured by ferro-electric cells arranged in a matrix form.
11. The semiconductor integrated circuit device according to claim 1, wherein the memory cell array is configured by series connected parallel-TC unit type ferro-electric cells arranged in a matrix form.
12. The semiconductor integrated circuit device according to claim 1, wherein the memory cell array is configured by dynamic cells arranged in a matrix form.
13. A data write method of a semiconductor integrated circuit device which includes
a memory cell array for which a data write operation is to be performed by an external write enable signal;
an address transition detecting circuit which detects transition of a column address signal, the column address signal being used to specify a column address of the memory cell array;
a control circuit having a timeout circuit, the control circuit which generates an internal circuit control signal of desired length used to control column access to the memory cell array based on a result of detection by the address transition detecting circuit; and
a column selection line whose selection time is controlled by the control circuit, compnsing:
latching the column address signal used for selection of the column selection line without clock-operating the external write enable signal in a period of time in which the column selection line is selected at a write operation time,
wherein a time for fetching data to be written at the write operation time is determined only by a transition time of the column address signal.
14. A data write method of a semiconductor integrated circuit device which includes
a memory cell array;
a first address transition detecting circuit which detects at least one of transition of a chip enable signal, transition of a row address signal and a column address signal and transition of a write enable signal, the chip enable signal being used to specify start of the operation of the memory cell array, the row address signal being used to specify a row address of the memory cell array, the column address signal being used to specify a column address of the memory cell array, the write enable signal being used to specify a write operation of the memory cell array;
a first control circuit having a timeout circuit, the first control circuit which generates an internal circuit control signal of desired length used to control row access to the memory cell array based on a result of detection by the first address transition detecting circuit; a second address transition detecting circuit which detects only transition of the column address signal;
a second control circuit which controls column access to the memory cell array based on a result of detection by the second address transition detecting circuit;
a column selection line whose selection time is controlled by the second control circuit; and
a mode determination circuit which determines start of a column access mode to generate a mode determination signal and set a column access operation when transition of the column address signal is detected by the second address transition detecting circuit in a case where a condition that the column access operation of the memory cell array is acceptable and then determines an end of the column access mode and sets a standby state in a case where transition of one of the row address and a predetermined address is detected, comprising:
latching the column address signal used for selection of the column selection line in a period of time in which the column selection line is selected when the mode determination circuit determines the column access mode at a write operation time.
15. The data write method according to claim 13, wherein the semiconductor integrated circuit device further includes
a buffer circuit which is supplied with the column address signal and power supply voltage and whose activenon-active state is controlled by supplynon supply of the power supply voltage,
a latch circuit which latches an output signal of the buffer circuit and
a switch which is provided between the buffer circuit and the latch circuit and whose conduction state is controlled by a first control signal to makebreak a connection between the buffer circuit and the latch circuit according to the conduction state thereof,
wherein the switch is set in the non-conductive state to disconnect the buffer circuit from the latch circuit when the address transition detecting circuit detects transition of the column address signal.
16. The data write method according to claim 15, wherein the latch circuit includes
a first latch which latches the output signal of the buffer circuit in response to a second control signal and
a second latch which latches an output signal of the first latch in response to a third control signal and generates a complementary signal.
17. The data write method according to claim 16, wherein the address transition detecting circuit detects transition of the column address signal based on the output signal of the first latch.
18. The data write method according to claim 14, wherein the condition that the column access operation of the memory cell array is acceptable is set based on a sense amplifier enable signal, the sense amplifier enable signal is output from a sense amplifier control circuit which is controlled by the first control circuit.