使用Arduino Nano 33 BLE Sense和Tensorflow IMU分类器实现人体动作检测。

代码示例：

//导入必要的文件 #include #include #include #include

//定义常量 #define INT_PIN 2
#define LED_PIN 13
#define DATASET_SIZE 962 //数据集大小 #define NUM_CLASSES 6 //类别数 #define MODEL_SIZE 34432 //模型大小

//定义TensorFlow模型变量 unsigned char model_data[MODEL_SIZE] = {...}; namespace { constexpr int kTensorArenaSize = 60 * 1024; uint8_t tensor_arena[kTensorArenaSize]; }

//初始化函数 void setup() { APDS9960.begin(); LSM9DS1.begin();

pinMode(INT_PIN, INPUT);
pinMode(LED_PIN, OUTPUT);

//加载模型
tflite::MicroErrorReporter micro_error_reporter;
static tflite::MicroOpResolver<6> micro_op_resolver;
static tflite::MicroInterpreter static_micro_interpreter(
    kTensorArenaSize, tensor_arena, micro_op_resolver,
    µ_error_reporter);
TfLiteStatus allocate_error = static_micro_interpreter.AllocateTensors();
if (allocate_error != kTfLiteOk) {
    Serial.println("AllocateTensors() error.");
}

//设置输入和输出张量
TfLiteTensor* input = static_micro_interpreter.input(0);
TfLiteTensor* output = static_micro_interpreter.output(0);
input->type = kTfLiteInt8;
input->dims->data[0] = 1;
input->dims->data[1] = DATASET_SIZE;
input->dims->data[2] = NUM_CLASSES;
input->scale = 0.01;
input->zero_point = 0;
output->type = kTfLiteInt8;
output->dims->data[0] = 1;
output->dims->data[1] = NUM_CLASSES;
output->scale = 0.01;
output->zero_point = 0;

}

//循环函数 void loop() { float ax, ay, az; float gx, gy, gz;

//获取传感器数据
LSM9DS1.readAcceleration(&ax, &ay, &az);
LSM9DS1.readGyroscope(&gx, &gy, &gz);

//将