Good evening! I have used this code for accelerometer-based fall detection.
#include "TensorFlowLite.h"
#include "fallmodel.h"
#include "tensorflow/lite/micro/kernels/micro_ops.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/micro/testing/micro_test.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"
#include <Arduino.h>
#include <Arduino_LSM9DS1.h>
#include <ArduinoBLE.h>
BLEService fallService("5321");
BLEBoolCharacteristic fallDetectionBool("0001", BLERead | BLENotify);
tflite::MicroErrorReporter tflErrorReporter;
const tflite::Model* tflModel = nullptr;
tflite::MicroInterpreter* tflInterpreter = nullptr;
TfLiteTensor* tflInputTensor = nullptr;
TfLiteTensor* tflOutputTensor = nullptr;
const int tensorArenaSize = 32 * 1024;
uint8_t tensorArena[tensorArenaSize];
float y_pred;
int arrayIndex = 0;
float kTargetHz = 31.25;
int skipCounter = 1;
int sampleEveryN;
boolean dataFull = false;
const int buttonPin = 12;
const int notificationPin = 8;
boolean buttonState;
boolean currentButtonState;
boolean lastButtonState;
boolean buttonPressed;
unsigned long lastDebounceTime;
unsigned long lastFallTime;
boolean fell;
void setup() {
// initialize serial communication at 9600 bits per second:
Serial.begin(9600);
delay(7000);
tflModel = tflite::GetModel(model);
if (tflModel->version() != TFLITE_SCHEMA_VERSION) {
Serial.println("Model schema mismatch!");
while (1);
}
static tflite::MicroMutableOpResolver<5> resolver; // NOLINT
resolver.AddMaxPool2D();
resolver.AddConv2D();
resolver.AddFullyConnected();
resolver.AddSoftmax();
resolver.AddReshape();
tflInterpreter = new tflite::MicroInterpreter(tflModel, resolver, tensorArena, tensorArenaSize, &tflErrorReporter);
// Allocate memory for the model's input and output tensors
TfLiteStatus allocate_status = tflInterpreter->AllocateTensors();
if (allocate_status != kTfLiteOk) {
Serial.println("AllocateTensors() failed");
return;
}
// Get pointers for the model's input and output tensors
tflInputTensor = tflInterpreter->input(0);
tflOutputTensor = tflInterpreter->output(0);
if (!IMU.begin()) {
Serial.println("Failed to initialize IMU");
}
if (!BLE.begin())
{
Serial.println("starting BLE failed!");
while (1);
}
//set up accelerometer range and frequency
IMU.setAccelFS(3);
IMU.setAccelODR(3);
IMU.setContinuousMode();
sampleEveryN = static_cast<int>(roundf(IMU.getAccelODR() / kTargetHz));
//setting up BLE
String BLEAddress = BLE.address();
Serial.println ("Address: " + BLEAddress);
BLE.setDeviceName("Fall Detector");
BLE.setAppearance(1088);
BLE.setLocalName("BLE Fall Detection");
BLE.setAdvertisedService(fallService);
fallService.addCharacteristic(fallDetectionBool);
BLE.addService(fallService);
fallDetectionBool.writeValue(false);
BLE.advertise();
Serial.println("Looking for connections");
BLE.setEventHandler(BLEConnected, bleConnectHandler);
BLE.setEventHandler(BLEDisconnected, bleDisconnectHandler);
pinMode(buttonPin, INPUT_PULLUP);
pinMode(notificationPin, OUTPUT);
Serial.println("Initialized");
}
void loop() {
BLEDevice central = BLE.central();
while (IMU.accelerationAvailable()) {
float aX, aY, aZ;
IMU.readAcceleration(aZ, aY, aX);
//skipping to match the frequency of the data used to train the model
if (skipCounter != sampleEveryN) {
skipCounter++;
continue;
}
tflInputTensor->data.f[3 * arrayIndex] = aX/10;
tflInputTensor->data.f[3 * arrayIndex + 1] = aY/10;
tflInputTensor->data.f[3 * arrayIndex + 2] = aZ/10;
//x[arrayIndex] = aX;
//y[arrayIndex] = aY;
//z[arrayIndex] = aZ;
skipCounter = 1;
arrayIndex++;
if (arrayIndex > 74) {
dataFull = true;
break;
}
}
currentButtonState = !digitalRead(buttonPin); //read state of button
if (currentButtonState != lastButtonState) {
lastDebounceTime = millis();
}
if (millis() - lastDebounceTime > 50) {
if (currentButtonState != buttonState) {
buttonState = currentButtonState;
if (buttonState == LOW) {
buttonPressed = true;
Serial.println("button pressed");
}
}
}
lastButtonState = currentButtonState;
if (dataFull) {
// Serial.println(tflInputTensor->data.f[0]);
TfLiteStatus invokeStatus = tflInterpreter->Invoke(); //run the model
arrayIndex = 0;
dataFull = false;
if (invokeStatus != kTfLiteOk) {
Serial.println("invoke failed");
}
Serial.println("");
y_pred = tflOutputTensor->data.f[1]; //model's prediction on accel data
Serial.println(y_pred);
if (y_pred > 0.7) {
//fall has been detected by the model
Serial.println("Fall");
lastFallTime = millis();
fell = true;
digitalWrite(notificationPin, HIGH);
}
}
if (fell) {
//options after a fall is dected
if (millis() - lastFallTime > 30 * 1000) {
//alerting phone app as no button press for 30 seconds after fall detected
if (central) {
fallDetectionBool.writeValue(true);
}
digitalWrite(notificationPin, LOW);
fell = false;
}
else if (buttonPressed) {
//button is pressed during initial time period and fall alert is deactivated
digitalWrite(notificationPin, LOW);
if (central) {
fallDetectionBool.writeValue(false);
}
fell = false;
}
else {
//initial 30 second time period when buzzer is on
digitalWrite(notificationPin, HIGH);
}
}
else if (buttonPressed) {
//if the button is pressed when a fall is not detected activate the fall timer
lastFallTime = millis();
fell = true;
digitalWrite(notificationPin, HIGH);
}
buttonPressed = false;
}
void bleConnectHandler(BLEDevice central) {
Serial.println("connect");
}
void bleDisconnectHandler(BLEDevice central) {
Serial.println("disconnect");
}
I am getting these errors:
error: 'class LSM9DS1Class' has no member named 'setAccelFS'
IMU.setAccelFS(3);
error: 'class LSM9DS1Class' has no member named 'setAccelODR'
IMU.setAccelODR(3);
error: 'class LSM9DS1Class' has no member named 'getAccelODR'
sampleEveryN = static_cast(roundf(IMU.getAccelODR() / kTargetHz));
I have the necessary libraries installed but still, I am not able to receive the output. Could you please suggest a way to solve the issue?
Thank you!
