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--- pulse_oximeter_sensor [2023/07/27 10:28]
wangzihao [5. 参考案例]
+++ pulse_oximeter_sensor [2023/08/08 16:06] (当前版本)
zili [5. 参考案例]
@@ 行 68: / 行 68: @@
 ### 5. 参考案例
-在Thonny使用Mircopython编写程序控制RP2040控制Max30102读取心率数据
+**在Thonny使用Mircopython编写程序控制RP2040控制Max30102读取心率数据**
 ####Max30102芯片介绍
@@ 行 83: / 行 83: @@
 ####程序代码
+程序文件需要文件
+- _init_.py
+- circular_buffer.py
+- spo2cal.py
+- HR_SpO2.py
+前面两个文件可在github上面下载https://github.com/n-elia/MAX30102-MicroPython-driver/tree/main/max30102
+spo2cal.py程序如下
+<code verilog>
+    # -*-coding:utf-8
+    # 25 samples per second (in algorithm.h)
+    SAMPLE_FREQ = 25
+    # taking moving average of 4 samples when calculating HR
+    # in algorithm.h, "DONOT CHANGE" comment is attached
+    MA_SIZE = 4
+    # sampling frequency * 4 (in algorithm.h)
+    BUFFER_SIZE = 100
+    # this assumes ir_data and red_data as np.array
+    def calc_hr_and_spo2(ir_data, red_data):
+        """
+        By detecting  peaks of PPG cycle and corresponding AC/DC
+        of red/infra-red signal, the an_ratio for the SPO2 is computed.
+        """
+        # get dc mean
+        ir_mean = int(sum(ir_data) / len(ir_data))
+        # remove DC mean and inver signal
+        # this lets peak detecter detect valley
+        x = [ir_mean - x for x in ir_data]
+        # 4 point moving average
+        # x is np.array with int values, so automatically casted to int
+        for i in range(len(x) - MA_SIZE):
+            x[i] = sum(x[i:i + MA_SIZE]) / MA_SIZE
+        # calculate threshold
+        n_th = int(sum(x) / len(x))
+        n_th = 30 if n_th < 30 else n_th  # min allowed
+        n_th = 60 if n_th > 60 else n_th  # max allowed
+        ir_valley_locs, n_peaks = find_peaks(x, BUFFER_SIZE, n_th, 4, 15)
+        # print(ir_valley_locs[:n_peaks], ",", end="")
+        peak_interval_sum = 0
+        if n_peaks >= 2:
+            for i in range(1, n_peaks):
+                peak_interval_sum += (ir_valley_locs[i] - ir_valley_locs[i - 1])
+            peak_interval_sum = int(peak_interval_sum / (n_peaks - 1))
+            hr = int(SAMPLE_FREQ * 60 / peak_interval_sum)
+            hr_valid = True
+        else:
+            hr = -999  # unable to calculate because # of peaks are too small
+            hr_valid = False
+        # ---------spo2---------
+        # find precise min near ir_valley_locs (???)
+        exact_ir_valley_locs_count = n_peaks
+        # find ir-red DC and ir-red AC for SPO2 calibration ratio
+        # find AC/DC maximum of raw
+        # FIXME: needed??
+        for i in range(exact_ir_valley_locs_count):
+            if ir_valley_locs[i] > BUFFER_SIZE:
+                spo2 = -999  # do not use SPO2 since valley loc is out of range
+                spo2_valid = False
+                return hr, hr_valid, spo2, spo2_valid
+        i_ratio_count = 0
+        ratio = []
+        # find max between two valley locations
+        # and use ratio between AC component of Ir and Red DC component of Ir and Red for SpO2
+        red_dc_max_index = -1
+        ir_dc_max_index = -1
+        for k in range(exact_ir_valley_locs_count - 1):
+            red_dc_max = -16777216
+            ir_dc_max = -16777216
+            if ir_valley_locs[k + 1] - ir_valley_locs[k] > 3:
+                for i in range(ir_valley_locs[k], ir_valley_locs[k + 1]):
+                    if ir_data[i] > ir_dc_max:
+                        ir_dc_max = ir_data[i]
+                        ir_dc_max_index = i
+                    if red_data[i] > red_dc_max:
+                        red_dc_max = red_data[i]
+                        red_dc_max_index = i
+                red_ac = int((red_data[ir_valley_locs[k + 1]] - red_data[ir_valley_locs[k]]) * (red_dc_max_index - ir_valley_locs[k]))
+                red_ac = red_data[ir_valley_locs[k]] + int(red_ac / (ir_valley_locs[k + 1] - ir_valley_locs[k]))
+                red_ac = red_data[red_dc_max_index] - red_ac  # subtract linear DC components from raw
+                ir_ac = int((ir_data[ir_valley_locs[k + 1]] - ir_data[ir_valley_locs[k]]) * (ir_dc_max_index - ir_valley_locs[k]))
+                ir_ac = ir_data[ir_valley_locs[k]] + int(ir_ac / (ir_valley_locs[k + 1] - ir_valley_locs[k]))
+                ir_ac = ir_data[ir_dc_max_index] - ir_ac  # subtract linear DC components from raw
+                nume = red_ac * ir_dc_max
+                denom = ir_ac * red_dc_max
+                if (denom > 0 and i_ratio_count < 5) and nume != 0:
+                    # original cpp implementation uses overflow intentionally.
+                    # but at 64-bit OS, Pyhthon 3.X uses 64-bit int and nume*100/denom does not trigger overflow
+                    # so using bit operation ( &0xffffffff ) is needed
+                    ratio.append(int(((nume * 100) & 0xffffffff) / denom))
+                    i_ratio_count += 1
+        # choose median value since PPG signal may vary from beat to beat
+        ratio = sorted(ratio)  # sort to ascending order
+        mid_index = int(i_ratio_count / 2)
+        ratio_ave = 0
+        if mid_index > 1:
+            ratio_ave = int((ratio[mid_index - 1] + ratio[mid_index]) / 2)
+        else:
+            if len(ratio) != 0:
+                ratio_ave = ratio[mid_index]
+        # why 184?
+        # print("ratio average: ", ratio_ave)
+        if ratio_ave > 2 and ratio_ave < 184:
+            # -45.060 * ratioAverage * ratioAverage / 10000 + 30.354 * ratioAverage / 100 + 94.845
+            spo2 = -45.060 * (ratio_ave ** 2) / 10000.0 + 30.054 * ratio_ave / 100.0 + 94.845
+            spo2_valid = True
+        else:
+            spo2 = -999
+            spo2_valid = False
+        return hr - 20, hr_valid, spo2, spo2_valid
+    def find_peaks(x, size, min_height, min_dist, max_num):
+        """
+        Find at most MAX_NUM peaks above MIN_HEIGHT separated by at least MIN_DISTANCE
+        """
+        ir_valley_locs, n_peaks = find_peaks_above_min_height(x, size, min_height, max_num)
+        ir_valley_locs, n_peaks = remove_close_peaks(n_peaks, ir_valley_locs, x, min_dist)
+        n_peaks = min([n_peaks, max_num])
+        return ir_valley_locs, n_peaks
+    def find_peaks_above_min_height(x, size, min_height, max_num):
+        """
+        Find all peaks above MIN_HEIGHT
+        """
+        i = 0
+        n_peaks = 0
+        ir_valley_locs = []  # [0 for i in range(max_num)]
+        while i < size - 1:
+            if x[i] > min_height and x[i] > x[i - 1]:  # find the left edge of potential peaks
+                n_width = 1
+                # original condition i+n_width < size may cause IndexError
+                # so I changed the condition to i+n_width < size - 1
+                while i + n_width < size - 1 and x[i] == x[i + n_width]:  # find flat peaks
+                    n_width += 1
+                if x[i] > x[i + n_width] and n_peaks < max_num:  # find the right edge of peaks
+                    # ir_valley_locs[n_peaks] = i
+                    ir_valley_locs.append(i)
+                    n_peaks += 1  # original uses post increment
+                    i += n_width + 1
+                else:
+                    i += n_width
+            else:
+                i += 1
+        return ir_valley_locs, n_peaks
+    def remove_close_peaks(n_peaks, ir_valley_locs, x, min_dist):
+        """
+        Remove peaks separated by less than MIN_DISTANCE
+        """
+        # should be equal to maxim_sort_indices_descend
+        # order peaks from large to small
+        # should ignore index:0
+        sorted_indices = sorted(ir_valley_locs, key=lambda i: x[i])
+        sorted_indices.reverse()
+        # this "for" loop expression does not check finish condition
+        # for i in range(-1, n_peaks):
+        i = -1
+        while i < n_peaks:
+            old_n_peaks = n_peaks
+            n_peaks = i + 1
+            # this "for" loop expression does not check finish condition
+            # for j in (i + 1, old_n_peaks):
+            j = i + 1
+            while j < old_n_peaks:
+                n_dist = (sorted_indices[j] - sorted_indices[i]) if i != -1 else (sorted_indices[j] + 1)  # lag-zero peak of autocorr is at index -1
+                if n_dist > min_dist or n_dist < -1 * min_dist:
+                    sorted_indices[n_peaks] = sorted_indices[j]
+                    n_peaks += 1  # original uses post increment
+                j += 1
+            i += 1
+        sorted_indices[:n_peaks] = sorted(sorted_indices[:n_peaks])
+        return sorted_indices, n_peaks
+    if __name__ == "__main__":
+        hr, hrb, sp, spb = calc_hr_and_spo2([12853, 15573, 15580, 15586, 15587, 15567, 15520, 15480, 15464, 15460, 15462, 15466, 15473, 15479, 15485, 15490, 15495, 15503, 15512, 15518, 15521, 15521, 15518, 15517, 15522, 15527, 15536, 15547, 15558, 15568, 15577, 15587, 15594, 15604, 15610, 15616, 15620, 15624, 15625, 15615, 15576, 15531, 15508, 15500, 15502, 15509, 15516, 15523, 15528, 15533, 15538, 15547, 15556, 15564, 15564, 15560, 15556, 15556, 15559, 15564, 15570, 15579, 15588, 15599, 15610, 15619, 15628, 15635, 15642, 15649, 15655, 15662, 15669, 15672, 15661, 15621, 15571, 15546, 15537, 15538, 15545, 15553, 15560, 15565, 15570, 15577, 15585, 15593, 15600, 15601, 15597, 15592, 15591, 15594, 15600, 15608, 15617, 15626, 15633, 15640], [12258, 14318, 14322, 14324, 14326, 14317, 14299, 14284, 14280, 14279, 14280, 14283, 14285, 14288, 14292, 14294, 14297, 14299, 14302, 14304, 14305, 14305, 14304, 14304, 14306, 14308, 14311, 14316, 14321, 14325, 14329, 14333, 14329, 14329, 14332, 14335, 14336, 14338, 14338, 14333, 14315, 14295, 14286, 14283, 14285, 14288, 14292, 14295, 14297, 14298, 14301, 14305, 14309, 14312, 14312, 14310, 14308, 14308, 14309, 14312, 14315, 14318, 14322, 14327, 14332, 14336, 14341, 14344, 14347, 14350, 14351, 14354, 14357, 14359, 14353, 14335, 14313, 14304, 14300, 14302, 14305, 14309, 14312, 14314, 14316, 14319, 14323, 14326, 14329, 14329, 14326, 14325, 14324, 14326, 14328, 14332, 14336, 14341, 14345, 14349])
+        print("hr detected:", hrb)
+        print("sp detected:", spb)
+        if (hrb == True and hr != -999):
+            hr2 = int(hr)
+            print("Heart Rate : ", hr2)
+        if (spb == True and sp != -999):
+            sp2 = int(sp)
+            print("SPO2       : ", sp2)
+  </code>
+HR_SpO2.py程序如下：
+<code verilog>
+    from machine import SoftI2C, Pin, Timer
+    from utime import ticks_diff, ticks_us
+    from max30102 import MAX30102, MAX30105_PULSE_AMP_MEDIUM
+    from spo2cal import calc_hr_and_spo2
+    BEATS = 0  # 存储心率
+    FINGER_FLAG = False  # 默认表示未检测到手指
+    SPO2 = 0  # 存储血氧
+    TEMPERATURE = 0  # 存储温度
+    def display_info(t):
+        # 如果没有检测到手指，那么就不显示
+        if FINGER_FLAG is False:
+            return
+        print('Heart Rate: ', BEATS, " SpO2：", SPO2, " Temperture：", TEMPERATURE)
+    def main():
+        global BEATS, FINGER_FLAG, SPO2, TEMPERATURE  # 如果需要对全局变量修改，则需要global声明
+        # 创建I2C对象(检测MAX30102)
+        i2c = SoftI2C(sda=Pin(16), scl=Pin(17), freq=400000)  # Fast: 400kHz, slow: 100kHz
+        # 创建传感器对象
+        sensor = MAX30102(i2c=i2c)
+        # 检测是否有传感器
+        if sensor.i2c_address not in i2c.scan():
+            print("没有找到传感器")
+            return
+        elif not (sensor.check_part_id()):
+            # 检查传感器是否兼容
+            print("检测到的I2C设备不是MAX30102或者MAX30105")
+            return
+        else:
+            print("传感器已识别到")
+        # 配置
+        sensor.setup_sensor()
+        sensor.set_sample_rate(400)
+        sensor.set_fifo_average(8)
+        sensor.set_active_leds_amplitude(MAX30105_PULSE_AMP_MEDIUM)
+        t_start = ticks_us()  # Starting time of the acquisition
+        MAX_HISTORY = 32
+        history = []
+        beats_history = []
+        beat = False
+        red_list = []
+        ir_list = []
+        while True:
+            sensor.check()
+            if sensor.available():
+                # FIFO 先进先出，从队列中取数据。都是整形int
+                red_reading = sensor.pop_red_from_storage()
+                ir_reading = sensor.pop_ir_from_storage()
+                if red_reading < 1000:
+                    print('No finger')
+                    FINGER_FLAG = False  # 表示没有放手指
+                    continue
+                else:
+                    FINGER_FLAG = True  # 表示手指已放
+                # 计算心率
+                history.append(red_reading)
+                # 为了防止列表过大，这里取列表的后32个元素
+                history = history[-MAX_HISTORY:]
+                # 提取必要数据
+                minima, maxima = min(history), max(history)
+                threshold_on = (minima + maxima * 3) // 4   # 3/4
+                threshold_off = (minima + maxima) // 2      # 1/2
+                if not beat and red_reading > threshold_on:
+                    beat = True
+                    t_us = ticks_diff(ticks_us(), t_start)
+                    t_s = t_us/1000000
+                    f = 1/t_s
+                    bpm = f * 60
+                    if bpm < 500:
+                        t_start = ticks_us()
+                        beats_history.append(bpm)
+                        beats_history = beats_history[-MAX_HISTORY:]   # 只保留最大30个元素数据
+                        BEATS = round(sum(beats_history)/len(beats_history), 2)  # 四舍五入
+                if beat and red_reading < threshold_off:
+                    beat = False
+                # 计算血氧
+                red_list.append(red_reading)
+                ir_list.append(ir_reading)
+                # 最多 只保留最新的100个
+                red_list = red_list[-100:]
+                ir_list = ir_list[-100:]
+                # 计算血氧值
+                if len(red_list) == 100 and len(ir_list) == 100:
+                    hr, hrb, sp, spb = calc_hr_and_spo2(red_list, ir_list)
+                    if hrb is True and spb is True:
+                        if sp != -999:
+                            SPO2 = int(sp)
+                # 计算温度
+                TEMPERATURE = sensor.read_temperature()
+    if __name__ == '__main__':
+        tim = Timer(period=1000, mode=Timer.PERIODIC, callback=display_info)
+        main()
+  </code>
 ####运行效果
 {{ :spo2_test.png?700 |}}