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    ‎04-27-2022 09:44 AM
    Selecting the right part BMA456 is 16bit, digital, triaxial acceleration sensor with intelligent on-chip motion triggered interrupt features optimized for wearable and hearable applications. BMA456 support different advanced features via different configure file. Table 1 shows an overview of the features. Parameter BMA456 Digital resolution 16bit Range and sensitivity +/-2g: 16384LSB/g +/-4g: 8192LSB/g +/-8g: 4096LSB/g +/-4g: 2048LSB/g Zero-g offset(typ.) +/-20mg Noise density(typ.) 120µg/√Hz Bandwidths 5Hz …684Hz Interfaces SPI & I2C, 2 x digital interrupt pins Supply voltage VDD: 1.62 to 3.6V VDDIO: 1.2 to 3.6V LGA package(mm3) 2.0 x 2.0 x 0.65 Feature/Interrupts Step Counter/Step detector (optimized for wearables) Activity recognition: running, walking, still Tilt on wrist Tab/Double Tab Any-motion/No-motion High-g/ low-g Table 1: Overview BMA456 features Common characteristics The main characteristics of this product family are: Key features: 2.0 x 2.0 mm² size Pin to pin compatibility with all other 2.0 x 2.0 accelerometers from Bosch Sensortec SPI or I²C interface Configurable range from ±2G to ±16g Configurable output data rate up to 1.6kHz Integrated 1kB FIFO Auxiliary I²C interface for connecting external magnetometer, including data synchronization Built-in smart interrupt controller, with features such as step-counter which offers high performance for all wearing positions, including wrist-worn. BMA456 parameters BMA456 offers higher performance and stability in a smaller package. See the complete description in Table 2. Parameter BMA456 Units Height 0,65 mm Digital resolution 16 bits Zero-g offset (typ.) ±20 mg Noise density (typ.) 120 µg/√Hz TCO (X&Y axis) 0.2 mg/K TCO (Z axis) 0.35 mg/K TCS 0.005 %/K Cross Axis Sensitivity 0.5 % Table 2: BMA456 parameter value Available evaluation tools and software To best to evaluate the products from the BMA456 family, we recommend the following combination of evaluation tools: COINES Desktop software Application board 3.0 Sensor Shuttle board BMA456 Shuttle board. Layout recommendations Because the BMA4xy sensor family contains tiny mechanical structure inside the package, care must be taken during the layout phase to ensure the best performance. The complete handling and soldering guide can be found on the Bosch Sensortec's website. First power-on After powering the sensor for the first time, the initial specs would be to test for communication with the device. This can be done simply by reading the chip identification code in the register 0x00. See below for the expected values: Device Chip ID BMA456 0x16 Table 3: Chip IDs of the BMA4xy product family Here is some sample code on how to perform this test, based on BMA456, using the COINES software as the host. /*!  * @brief This internal API is used to initializes the bma456 and verify the  *communication by reading the chip id.  *  * @param[in] void  * @return void  *  */ static void init_comm_test_bma456(void) {     int8_t rslt;                    struct bma4_dev bma456dev = { 0 };       rslt = bma4_interface_init(&bma456dev, BMA4_I2C_INTF, BMA45X_VARIANT);     rslt = bma456_init(&bma456devbma425dev);     if (rslt == BMA4_OK)     {         printf("BMA456 Initialization Success!\n");         printf("Test #1: Communication. PASSED. Chip ID 0x%x\n", bma456dev.chip_id);     }       else     {         char err_string[255];         printf("BMA456 Initialization Failure!\n");                                        if (BMA4_E_INVALID_SENSOR == rslt)         {              sprintf(err_string, "Test #1: Communication. FAILED. Expected Chip ID: 0x%x. Received 0x%x\n",\                             BMA456_CHIP_ID, bma456dev.chip_id);         }         else         {              sprintf(err_string, "Test #1: Communication. FAILED. No response from the sensor.");         }                                        coines_exit_error(err_string);     }     coines_delay_msec(100); } How to test the sensor's functionality The BMA4xy series of accelerometers feature a fully integrated and motionless self-test procedure on the ASIC itself. When the self-test is triggered, the accelerometer uses electric fields to physically move the electrodes in all directions, senses the deflection and compares it with the expected output. Therefore, the built-in self-test features is the recommended way to test the sensor's functionality. Here is some sample code on how to perform this self-test, based on BMA456, using the COINES software as the host. /*!  * @brief This internal API is used to test if the sensor is working by triggering the self-test.  *  * @param[in] void  * @return void  *  */ static void function_test_bma456(void) {     uint16_t rslt;     uint8_t testrslt;       rslt = bma4_perform_accel_selftest(&testrslt, &bma456dev);                    if ( 0 != rslt ) coines_exit_error("Test #2: Functionnality. FAILED. Unknown communication error\n");                if ( BMA4_SELFTEST_PASS == testrslt ) {         printf("Test #2: Functionnality. PASSED. Sensor self-test successful\n");     } else {         printf("Test #2: Functionnality. FAILED. Sensor self-test failed\n");     }                } How to test the sensor's performance There are 2 performance parameters that can easily be tested with the device motionless: offset and noise. See below for the typical values of the sensors. Note: Typical values are defined as ±1σ, which means that we expect 68.3% of sensors to fall within these values. Min/Max values are defined as ±3σ, which means 99.7% of sensors shall be within these values. Parameter BMA456 units Offset ±20 mg Noise 120 µg/√Hz Table 4: Typical performance of BMA456 For the offset, the test procedure is quite simple. With the device in a known position, such as a flat surface, calculate the average value for each axis, and substract the expected output (e.g. 0G, 0G, +1G) from the data. The result is the offset of the sensor. Here is some sample code on how to perform this test, based on BMA456, using the COINES software as the host. /*! Average value calculation */ double x_avg_mg=0; double y_avg_mg=0; double z_avg_mg=0; for (int i=0; i<1000; ++i) {     double xval, yval, zval;     /* ( 'datapoint' * '4G' * '2' * '1000mg') / ('scaling factor for 16 bits') */     xval = (((double)sens_data[i].x) * 4. * 2. * 1000) / pow(2,16);     yval = (((double)sens_data[i].y) * 4. * 2. * 1000) / pow(2,16);     zval = (((double)sens_data[i].z) * 4. * 2. * 1000) / pow(2,16);                                    x_avg_mg += xval / 1000.;     y_avg_mg += yval / 1000.;     z_avg_mg += zval / 1000.; }                /*! Offset Calculation */ double x_off_mg=x_avg_mg - X_REST_POSITION_MG; double y_off_mg=y_avg_mg - Y_REST_POSITION_MG; double z_off_mg=z_avg_mg - Z_REST_POSITION_MG;                if( OFFSET_THRESHOLD_MG > x_off_mg && OFFSET_THRESHOLD_MG > y_off_mg && \                                OFFSET_THRESHOLD_MG > z_off_mg ) {     printf("Test #3: Performance. Offset. PASSED: X=%4.1lfmg Y=%4.1lfmg Z=%4.1lfmg Threshold <\        %4.1lf\n",       x_off_mg, y_off_mg, z_off_mg, OFFSET_THRESHOLD_MG); } else {     printf("Test #3: Performance. Offset. FAILED: X=%4.1lfmg Y=%4.1lfmg Z=%4.1lfmg Threshold <\ %4.1lf\n", x_off_mg, y_off_mg, z_off_mg, OFFSET_THRESHOLD_MG); } The noise calculation is a bit more complicated. First, subtract the offset from each datapoint. The RMS value can be calculated as the square root of the arithmetic mean of the squares of the noise values.  x_rms = sqrt[1/n( x_1^2 + x_2^2 + .... + x_n^2)] Since the noise value is affected by the bandwidth of the digital filter, we need to convert it back to noise density with the following formula. Note: this applied only to a second order filter noise_density = noise_RMS/[sqrt(1.22 x bandwidth)] Here is some sample code on how to perform this test, based on BMA456, using the COINES software as the host. /*! RMS Noise Calculation */ double x_rms_noise_mg=0; double y_rms_noise_mg=0; double z_rms_noise_mg=0; for (int i=0; i<1000; ++i) {     double xval, yval, zval;     /* ( 'datapoint' * '4G' * '2' * '1000mg') / ('scaling factor for 12 bits') */     xval = (((double)sens_data[i].x) * 4. * 2. * 1000.) / pow(2,16);     yval = (((double)sens_data[i].y) * 4. * 2. * 1000.) / pow(2,16);     zval = (((double)sens_data[i].z) * 4. * 2. * 1000.) / pow(2,16);                                    x_rms_noise_mg += pow(xval - x_avg_mg,2) / 1000.;     y_rms_noise_mg += pow(yval - y_avg_mg,2) / 1000.;     z_rms_noise_mg += pow(zval - z_avg_mg,2) / 1000.; }                /* rms noise is the square root of the arithmetic mean of the squares of the noise values */ x_rms_noise_mg = sqrt(x_rms_noise_mg); y_rms_noise_mg = sqrt(y_rms_noise_mg); z_rms_noise_mg = sqrt(z_rms_noise_mg);                /*! RMS Noise to RMS noise density convertion */ /* noise density = RMS noise  / sqrt ( 1.22 * bandwidth) */ /* BMA456 has 40.5Hz bandwidth at 100Hz normal mode */   double x_noise_dens_ug = 1000 * x_rms_noise_mg / sqrt(1.22 * 40.5); double y_noise_dens_ug = 1000 * y_rms_noise_mg / sqrt(1.22 * 40.5); double z_noise_dens_ug = 1000 * z_rms_noise_mg / sqrt(1.22 * 40.5);   if( NOISE_THRESHOLD_MG > x_noise_dens_ug &&                                NOISE_THRESHOLD_MG > y_noise_dens_ug &&                                NOISE_THRESHOLD_MG > z_noise_dens_ug ) {     printf("Test #3: Performance. Noise. PASSED: X=%4.1lfug/sqrt(Hz) Y=%4.1lfug/sqrt(Hz) \ Z=%4.1lfug/sqrt(Hz) Threshold < %4.1lf\n", x_noise_dens_ug, y_noise_dens_ug, z_noise_dens_ug,\ NOISE_THRESHOLD_MG); } else {     printf("Test #3: Performance. Noise. FAILED: X=%4.1lfug/sqrt(Hz) Y=%4.1lfug/sqrt(Hz) \   Z=%4.1lfug/sqrt(Hz) Threshold < %4.1lf\n", x_noise_dens_ug, y_noise_dens_ug,\ z_noise_dens_ug,NOISE_THRESHOLD_MG);   } Calibrating the sensor The first question to ask concerning calibration is whether it is required for the intended application. Accelerometer calibration mainly consists of calibrating the accelerometer's offset. The main impact for this is in tilt-sensing application, where the offset will induce an error in the measurement of the horizon. The accelerometer comes from the factory pre-trimmed, but the soldering process and PCB bending due to assembly can vary the offset, therefore it is preferable to calibrate the accelerometer after assembling the device into the device housing. Pre- and post-calibration accuracy     Sample code The calibration procedure is outlined in the inline calibration application note. Here is some sample code on how to perform this calibration, based on BMA456, using the COINES software as the host. Note : Although the concept is the same, the BMA4xy family of accelerometers does not include a built-in offset calculation on the ASIC. Since the offset are calculated inside of the Sensor API itself, it allows for a much more flexible target position.  rslt = bma456_init(&bma456dev); if (rslt == BMA4_OK) {     printf("BMA456 Initialization Success!\n"); } else {                  coines_exit_error("BMA456 Initialization Failure!\n"); } coines_delay_msec(100); rslt = bma456_write_config_file(&bma456dev); /* Enable the accelerometer */ rslt = bma4_set_accel_enable(BMA4_ENABLE, &bma456dev); /* Set the accel configurations */ struct bma4_accel_config accel_conf = { 0 }; accel_conf.odr = BMA4_OUTPUT_DATA_RATE_50HZ; accel_conf.bandwidth = BMA4_ACCEL_NORMAL_AVG4; accel_conf.perf_mode = BMA4_CIC_AVG_MODE; accel_conf.range = BMA4_ACCEL_RANGE_8G; rslt = bma4_set_accel_config(&accel_conf, &bma456dev); dev.delay_us(20000, dev.intf_ptr);                         if (rslt == BMA4_OK) {     /* Set accel foc axis and it's sign (x, y, z, sign)*/ struct bma4_accel_foc_g_value g_value_foc = { 0, 0, 0, 0 };       rslt = bma4_perform_accel_foc( &g_value_foc, &bma456dev);                          if (rslt == BMA4_OK) {         printf("BMA456 perform FOC successful!\n");         /* Delay after performing Accel FOC */         dev->delay_us(30000, bma456dev ->intf_ptr);         /*! calculates the offset after compensation */         rslt = bma4_read_regs(BMA4_OFFSET_0_ADDR, data_array, 3, & bma456dev);         printf("Post-calibration offset : X=%4.1lfmg Y=%4.1lfmg Z=%4.1lfmg", data_array[0],\                data_array[1], data_array[3]);     } else {                  coines_exit_error("Unknown communication error. Exiting...\n");     } } Once the offsets are determined, they can be written into the NVM so that the sensor automatically compensates for the soldering offset even after physically removing the power. Here is some sample code on how to save calibration data to NVM, based on BMA456, using the COINES software as the host. /*!  * @brief This internal API is used to update the nvm content  *  * @param[in] void  * @return void  *  */ static void bma456_update_nvm(void) {     uint16_t rslt;     uint8_t data;       /* unlocks the NVM for writing */     data = 0x02;     bma4_write_regs(0x6A, &data, 1, &bma456dev);                    /* makes sure the BMA456 is not executing another command */ do {         rslt = bma4_read_regs(BMA4_STATUS_ADDR, &data, 1, &bma456dev);         if ( 0 != rslt ) coines_exit_error("Unknown communication error. Exiting...\n");     } while ( 0 == (data&0x10) );                    /* performs the writing of the NVM */     rslt = bma4_set_command_register( 0xA0,  &bma456dev);     if ( 0 != rslt ) coines_exit_error("Unknown communication error. Exiting...\n");                        /* wait for the command to be completed */ do {         rslt = bma4_read_regs(BMA4_STATUS_ADDR, &data, 1, &bma456dev);         if ( 0 != rslt ) coines_exit_error("Unknown communication error. Exiting...\n");     } while ( 0 == (data&0x10) );                    /* locks the NVM writing */     data = 0x00;     bma4_write_regs(0x6A, &data, 1, &bma456dev); }   Further reads Datasheets: BMA456 Datasheet Application notes: Inline calibration of accelerometers Handling, soldering and mounting instructions, Accelerometers HSMI  
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    ‎05-06-2022 04:05 PM
    The Self-Learning AI Software can recognize and track more than fifteen pre-learned fitness exercises and has the ability to learn new movements and fitness exercises created by you! Please find attached the ZIP file with the following contents: Never Skip Leg Day This session includes exercises where the sensor is mounted on the ankle and the focus will be on building leg strength. PDF document with all instructions Generated patterns Raw sample data Outdoor Bodyweight Workout This session includes exercises where the sensor is mounted on the arm. This workout is great for strengthening your core, arms and legs muscles. PDF document with all instructions Generated patterns Raw sample data Outdoor Core Workout This session includes exercises where the sensor is mounted on the ankle. This workout is great for strengthening your core. PDF document with all instructions Generated patterns Raw sample data Waist Tracking This session includes exercises where the sensor is mounted on the waist and the arm. PDF document with all instructions Generated patterns Raw sample data The self-learning AI firmware is available on our Nicla Sense ME board here. For more information about the list of virtual sensors, see the Nicla Sense ME Board examples.
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    ‎04-01-2021 10:22 AM
    Please find attached the two white papers for our BHI260AP self-learning AI sensor: White paper: Me, myself and AI How the new self-learning AI sensor pesonalizes your home workout Fitness tracking is presently experiencing a huge upswing in popularity. The market for fitness trackers and step counters has surged by 65% year-on-year [1], with just the Fitbit platform alone claiming nearly 30 million active users [2]. Current activity tracking devices have demonstrated the huge market potential of this segment and have laid the groundwork for next-generation AI-enabled devices to take centre stage as individual fitness tracking goes mainstream. White paper: Swim like a fish with Artificial Intelligence Combining AI and sensors to create a new generation of intelligent wearables Artificial intelligence (AI) is rapidly becoming a natural and integral part of our everyday lives – working silently and unnoticed in the background. AI makes sense of the multitude of data streaming in from various sensors to deliver detailed and precise insights, which have the potential to increase the utility of virtually all electronic devices on the market today. This defining technology can accurately determine whether the user of a device is walking, running, sitting, sleeping – or even swimming in real-time. This article explores how sensors synergize with AI in wearables to deliver valuable information to users – even in demanding environments like swimming pools.
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    ‎06-15-2020 05:05 AM
    For customer usage, you need to download the sensor driver package from the website to communicate with sensor after connecting the BMP388 chip to your developing board.  You can find the information from the following link https://github.com/BoschSensortec/BMP3-Sensor-API. The procedure of using BMP388 sensor API is presented in the following flow chart: The detailed example code for integration of API could be found in: https://github.com/BoschSensortec/BMP3-Sensor-API/blob/master/README.md Following steps need to be considered to ensure the API/sensor configured correctly. For reading the data information from API, the following example can follow 1.The following static function should be added into your own project static int64_t compensate_temperature static uint64_t compensate_pressure static double bmp3_pow static void parse_calib_data static double compensate_temperature_d static double compensate_pressure_d 2.Define the main function to print the data, the structure for BMP3 and put the trimming data into the correct position as follow. One example shown below calib_data->reg_calib_data.par_t1 = (int16_t)27402; calib_data->reg_calib_data.par_t2 = (int16_t)18868; calib_data->reg_calib_data.par_t3 = (int8_t)-10; calib_data->reg_calib_data.par_p1 = (int16_t)-244; calib_data->reg_calib_data.par_p2 = (int16_t)-3254; calib_data->reg_calib_data.par_p3 = (int8_t)35; calib_data->reg_calib_data.par_p4 = (int8_t)0; calib_data->reg_calib_data.par_p5 = (int16_t)25879; calib_data->reg_calib_data.par_p6 = (int16_t)31477; calib_data->reg_calib_data.par_p7 = (int8_t)-13; calib_data->reg_calib_data.par_p8 = (int8_t)-10; calib_data->reg_calib_data.par_p9 = (int16_t)16342; calib_data->reg_calib_data.par_p10 = (int8_t)29; calib_data->reg_calib_data.par_p11 = (int8_t)-60; 3.Give the correct default value to the uncompensated temperature and pressure, define the version to temperature and pressure. uncomp_data->pressure = 8241776; uncomp_data->temperature = 8329880; double temp = compensate_temperature_d(uncomp_data, calib_data); double press = compensate_pressure_d(uncomp_data, calib_data); double tempurature = temp; double pressure = press; 4.Print out the final value printf("Temperature\t Pressure\t\n"); printf("%0.2f\t\t %0.2f\t\t\n", tempurature, pressure); system("pause");  
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    ‎06-15-2020 04:52 AM
    General Description The BME280 is a combined digital humidity, pressure and temperature sensor based on proven sensing principles. The API can be downloaded from Github. The BSEC can be downloaded from the Bosch Sensortec website. Sensor Data The humidity sensor data is expressed as a percentage (10% - 90% at 0°C-65°C). The pressure sensor data is in hPa (300hPa - 1100hPa at 0°C-65°C). The temperature sensor data is in °C (-40°C - 85°C). The function bme280_get_sensor_data in the API is used to get the sensor data. Accuracy The accuracy represents how accurate the sensor can be under certain condition. Table 1 lists the accuracy of humidity sensor, pressure sensor, and temperature sensor. Pressure Sensor Drift The pressure sensor drift represents the error in the measured value. Basically, there are two drifts on the pressure part in the BME280, i.e. solder drift and long-term drift. Pressure Sensor TCO The TCO (offset temperature coefficient) is the change in the pressure signal caused by a change in the temperature. For the pressure sensor, the TCO is ±1.5 Pa/K, equivalent to ±12.6 cm at 1 °C temperature change, which means the pressure sensor data changes within ±1.5 Pa, with 1 °C temperature change at constant pressure. Working Mode The BME280 can be operated in three working modes: sleep mode, normal mode, and force mode, which can be selected using the mode [1:0] setting (in register 0xF4 [1:0], sleep mode -> b11, force mode -> b10 and b01, normal mode -> b11). The function bme280_set_sensor_mode in the API can also used to set power mode. During the measurement cycle, the BME280 measures temperature, pressure and humidity, with optional oversampling. After the measurement cycle, the pressure and temperature data can be passed through an optional IIR filter, which removes short-term fluctuations in pressure (e.g. caused by slamming a door). For humidity, such a filter is not needed and has not been implemented. Humidity/Pressure/Temperature OSR Oversampling can be enabled during the measurement, which can reduce noise but also consumes more power. Different sensors have different OSRs (oversampling rate). The osrs_h<2:0> settings are explained as follows: b000-> skip humidity data/no humidity b001-> oversamplingx1 b010-> oversamplingx2 b011-> oversamplingx4 b100-> oversamplingx8 b101, and other settings -> oversamplingx16  The osrs_p<4:2> settings are explained as follows: b000-> skip humidity data/no humidity b001-> oversamplingx1 b010-> oversamplingx2 b011-> oversamplingx4 b100-> oversamplingx8 b101, and other settings-> oversamplingx16 The osrs_t<7:5> settings are explained as follows: b000-> skip humidity data/no humidity b001-> oversamplingx1 b010-> oversamplingx2 b011-> oversamplingx4 b100-> oversamplingx8 b101, and other settings-> oversamplingx16   The function bme280_set_sensor_settings in the API can also be used to set the OSR for any sensor. See below for the example.   ... int8_t rslt; uint8_t settings_sel; dev->settings.osr_h = BME280_OVERSAMPLING_1X; dev->settings.osr_p = BME280_OVERSAMPLING_16X; dev->settings.osr_t = BME280_OVERSAMPLING_2X; ... settings_sel = BME280_OSR_PRESS_SEL | BME280_OSR_TEMP_SEL | BME280_OSR_HUM_SEL | BME280_FILTER_SEL; rslt = bme280_set_sensor_settings(settings_sel, dev); .... return rslt; ... Signal Filter The environmental pressure is subject to many short-term changes caused by, for example, door or window slamming, wind blowing into the sensor, etc. The BME280 features an internal IIR filter which can suppress these disturbances in the output data without causing additional interface traffic and processor workload. By setting the filter coefficient (c), the bandwidth of the temperature and pressure output signals can be effectively reduced and the resolution of the pressure and temperature output data can be increased to 20 bits. The output of the next measurement step is filtered using the following formula:  Where, data_filter_old is the data coming from the current filter memory. data_ADC is the data coming from current ADC acquisition. data_filtered is the new value of filter memory and the value that will be sent to the output registers.   The filter<4:2> settings are explained as follows: b000-> Filter off b001-> filter coefficient 2 b010-> filter coefficient 4 b011-> filter coefficient 8 b100, and other settings-> filter coefficient 16   The function bme280_set_sensor_settings in the API can also be used to set filter. See below for the example. ... int8_t rslt; uint8_t settings_sel; ... dev->settings.filter = BME280_FILTER_COEFF_16; ... settings_sel = BME280_OSR_PRESS_SEL | BME280_OSR_TEMP_SEL | BME280_OSR_HUM_SEL | BME280_FILTER_SEL; ... rslt = bme280_set_sensor_settings(settings_sel, dev); ... return rslt; ​
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