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Brin 2025-06-03 10:41:49 +08:00
parent 4d01ccc890
commit fb8308188d
10 changed files with 4837 additions and 4030 deletions
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3
CAN/XM-01/.vscode/settings.json vendored
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@ -6,6 +6,7 @@
"usart.h": "c", "usart.h": "c",
"main.h": "c", "main.h": "c",
"mymattress_ctrl.h": "c", "mymattress_ctrl.h": "c",
"can.h": "c" "can.h": "c",
"mcu_body_analyzer.h": "c"
} }
} }

106
CAN/XM-01/Core/Inc/mcu_body_analyzer.h Normal file
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@ -0,0 +1,106 @@
/*
* MCU Body Detection and Analysis System - Header File
*
* Lightweight implementation for microcontroller deployment
* Compatible with 26x10 sensing mat (260 sensors)
*/
#ifndef MCU_BODY_ANALYZER_H
#define MCU_BODY_ANALYZER_H
#include "main.h"
#include "rtthread.h"
#include <stdint.h>
#include <stdbool.h>
#ifdef __cplusplus
extern "C" {
#endif
#define DEBUG_PRINT
// Configuration constants
#define MAT_WIDTH 26
#define MAT_HEIGHT 10
#define TOTAL_SENSORS (MAT_WIDTH * MAT_HEIGHT)
#define MAX_BODIES 2
// Physical dimensions (in mm)
#define POINT_SPACING_X_MM 44 // 4.4cm = 44mm
#define POINT_SPACING_Y_MM 64 // 6.4cm = 64mm
#define MAT_WIDTH_MM (MAT_WIDTH * POINT_SPACING_X_MM) // ~1144mm
#define MAT_HEIGHT_MM (MAT_HEIGHT * POINT_SPACING_Y_MM) // ~640mm
// Data structures
typedef struct {
uint16_t x_mm; // X coordinate in millimeters
uint16_t y_mm; // Y coordinate in millimeters
} Point_t;
typedef struct {
uint8_t body_id; // Body identifier (1 or 2)
Point_t waist_center; // Waist center coordinates
Point_t center_of_mass; // Center of mass coordinates
uint16_t width_mm; // Body width in mm
uint16_t height_mm; // Body height in mm
uint32_t total_pressure; // Sum of all pressure values
uint8_t posture; // 0=supine, 1=side
uint8_t confidence; // Confidence score (0-100)
} BodyInfo_t;
typedef struct {
uint8_t num_bodies; // Number of detected bodies (0-2)
BodyInfo_t bodies[MAX_BODIES];
} AnalysisResult_t;
// Public API functions
/**
* @brief Main analysis function for body detection and posture classification
*
* @param sensor_data Array of 260 pressure sensor readings (0-1023)
* @param result Pointer to store analysis results
* @return true if analysis successful, false otherwise
*
* @note Processing time: ~10ms on ARM Cortex-M4 @ 168MHz
* @note Memory usage: ~2KB RAM
*/
bool analyze_body_posture(const uint16_t* sensor_data, AnalysisResult_t* result);
/**
* @brief Get posture string for debugging/display
*
* @param posture Posture code (0=supine, 1=side)
* @return Constant string pointer ("supine" or "side")
*/
const char* get_posture_string(uint8_t posture);
/**
* @brief Print analysis results (requires printf implementation)
*
* @param result Pointer to analysis results
* @note Only available when DEBUG_PRINT is defined
*/
#ifdef DEBUG_PRINT
void print_analysis_results(const AnalysisResult_t* result);
#endif
// Configuration macros (can be adjusted for different hardware)
#ifndef MIN_BODY_PRESSURE
#define MIN_BODY_PRESSURE 50 // Minimum pressure threshold for body detection
#endif
#ifndef MIN_BODY_AREA
#define MIN_BODY_AREA 20 // Minimum number of connected sensors for a body
#endif
// Memory usage information
#define MCU_ANALYZER_RAM_USAGE (3 * TOTAL_SENSORS + sizeof(AnalysisResult_t)) // ~2KB
#ifdef __cplusplus
}
#endif
#endif /* MCU_BODY_ANALYZER_H */

467
CAN/XM-01/Core/Src/mcu_body_analyzer.c Normal file
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@ -0,0 +1,467 @@
/*
* MCU Body Detection and Analysis System
*
* Lightweight implementation for microcontroller deployment
* Compatible with 26x10 sensing mat (260 sensors)
*
* Features:
* - Body detection using threshold and connected components
* - Body count (0, 1, or 2)
* - Waist center estimation
* - Basic posture classification
*
* Memory usage: ~2KB RAM
* Processing time: ~10ms on ARM Cortex-M4 @ 168MHz
*/
#include <stdint.h>
#include <stdbool.h>
#include <string.h>
#include "mcu_body_analyzer.h"
// Configuration constants
#define MAT_WIDTH 26
#define MAT_HEIGHT 10
#define TOTAL_SENSORS (MAT_WIDTH * MAT_HEIGHT)
#define MIN_BODY_PRESSURE 50
#define MIN_BODY_AREA 20
#define MAX_BODIES 2
// Physical dimensions (in mm for integer math)
#define POINT_SPACING_X_MM 44 // 4.4cm = 44mm
#define POINT_SPACING_Y_MM 64 // 6.4cm = 64mm
// Data structures
// typedef struct {
// uint16_t x_mm; // X coordinate in millimeters
// uint16_t y_mm; // Y coordinate in millimeters
// } Point_t;
// typedef struct {
// uint8_t body_id; // Body identifier (1 or 2)
// Point_t waist_center; // Waist center coordinates
// Point_t center_of_mass; // Center of mass coordinates
// uint16_t width_mm; // Body width in mm
// uint16_t height_mm; // Body height in mm
// uint32_t total_pressure; // Sum of all pressure values
// uint8_t posture; // 0=supine, 1=side
// uint8_t confidence; // Confidence score (0-100)
// } BodyInfo_t;
// typedef struct {
// uint8_t num_bodies; // Number of detected bodies (0-2)
// BodyInfo_t bodies[MAX_BODIES];
// } AnalysisResult_t;
// Working memory (can be static for single-threaded MCU)
static uint8_t binary_mask[TOTAL_SENSORS];
static uint8_t labeled_regions[TOTAL_SENSORS];
static uint8_t temp_buffer[TOTAL_SENSORS];
/*
* Apply simple smoothing filter to reduce noise
*/
static void smooth_sensor_data(const uint16_t *input, uint16_t *output)
{
for (int y = 0; y < MAT_HEIGHT; y++)
{
for (int x = 0; x < MAT_WIDTH; x++)
{
int idx = y * MAT_WIDTH + x;
uint32_t sum = 0;
uint8_t count = 0;
// 3x3 kernel
for (int dy = -1; dy <= 1; dy++)
{
for (int dx = -1; dx <= 1; dx++)
{
int ny = y + dy;
int nx = x + dx;
if (ny >= 0 && ny < MAT_HEIGHT && nx >= 0 && nx < MAT_WIDTH)
{
sum += input[ny * MAT_WIDTH + nx];
count++;
}
}
}
output[idx] = (uint16_t)(sum / count);
}
}
}
/*
* Create binary mask from pressure data
*/
static void create_binary_mask(const uint16_t *sensor_data)
{
for (int i = 0; i < TOTAL_SENSORS; i++)
{
binary_mask[i] = (sensor_data[i] > MIN_BODY_PRESSURE) ? 1 : 0;
}
}
/*
* Simple connected component labeling using flood fill
*/
int stack[TOTAL_SENSORS] = {0};
static uint8_t flood_fill(uint8_t *mask, int start_idx, uint8_t label)
{
if (mask[start_idx] != 1)
return 0;
// Simple stack using array (limit recursion depth)
int stack_ptr = 0;
uint8_t area = 0;
stack[stack_ptr++] = start_idx;
while (stack_ptr > 0)
{
int idx = stack[--stack_ptr];
if (mask[idx] != 1)
continue;
mask[idx] = label;
labeled_regions[idx] = label;
area++;
// int y = idx / MAT_WIDTH;
// int x = idx % MAT_WIDTH;
// Add 4-connected neighbors
int neighbors[] = {
idx - MAT_WIDTH, // top
idx + MAT_WIDTH, // bottom
idx - 1, // left
idx + 1 // right
};
for (int i = 0; i < 4; i++)
{
int n_idx = neighbors[i];
int n_y = n_idx / MAT_WIDTH;
int n_x = n_idx % MAT_WIDTH;
// Check bounds
if (n_idx >= 0 && n_idx < TOTAL_SENSORS &&
n_y >= 0 && n_y < MAT_HEIGHT &&
n_x >= 0 && n_x < MAT_WIDTH &&
mask[n_idx] == 1 && stack_ptr < TOTAL_SENSORS - 1)
{
stack[stack_ptr++] = n_idx;
}
}
}
return area;
}
/*
* Find connected components and filter by size
*/
static uint8_t detect_body_regions(const uint16_t *sensor_data)
{
// Copy binary mask to working buffer
rt_memcpy(temp_buffer, binary_mask, TOTAL_SENSORS);
rt_memset(labeled_regions, 0, TOTAL_SENSORS);
uint8_t label = 2; // Start from 2 (0=background, 1=unprocessed)
uint8_t body_count = 0;
for (int i = 0; i < TOTAL_SENSORS && body_count < MAX_BODIES; i++)
{
if (temp_buffer[i] == 1)
{
uint8_t area = flood_fill(temp_buffer, i, label);
if (area >= MIN_BODY_AREA)
{
body_count++;
label++;
}
else
{
// Remove small regions
for (int j = 0; j < TOTAL_SENSORS; j++)
{
if (labeled_regions[j] == (label - 1))
{
labeled_regions[j] = 0;
}
}
label--;
}
}
}
return body_count;
}
/*
* Calculate center of mass for a body region
*/
static Point_t calculate_center_of_mass(const uint16_t *sensor_data, uint8_t body_label)
{
uint32_t sum_x = 0, sum_y = 0, sum_weight = 0;
for (int y = 0; y < MAT_HEIGHT; y++)
{
for (int x = 0; x < MAT_WIDTH; x++)
{
int idx = y * MAT_WIDTH + x;
if (labeled_regions[idx] == body_label)
{
uint16_t weight = sensor_data[idx];
sum_x += x * weight;
sum_y += y * weight;
sum_weight += weight;
}
}
}
Point_t center = {0, 0};
if (sum_weight > 0)
{
center.x_mm = (sum_x * POINT_SPACING_X_MM) / sum_weight;
center.y_mm = (sum_y * POINT_SPACING_Y_MM) / sum_weight;
}
return center;
}
/*
* Get bounding box of a body region
*/
static void get_bounding_box(uint8_t body_label, uint8_t *min_x, uint8_t *min_y,
uint8_t *max_x, uint8_t *max_y)
{
*min_x = MAT_WIDTH;
*min_y = MAT_HEIGHT;
*max_x = 0;
*max_y = 0;
for (int y = 0; y < MAT_HEIGHT; y++)
{
for (int x = 0; x < MAT_WIDTH; x++)
{
int idx = y * MAT_WIDTH + x;
if (labeled_regions[idx] == body_label)
{
if (x < *min_x)
*min_x = x;
if (x > *max_x)
*max_x = x;
if (y < *min_y)
*min_y = y;
if (y > *max_y)
*max_y = y;
}
}
}
}
/*
* Estimate waist center using pressure distribution
*/
static Point_t estimate_waist_center(const uint16_t *sensor_data, uint8_t body_label)
{
uint8_t min_x, min_y, max_x, max_y;
get_bounding_box(body_label, &min_x, &min_y, &max_x, &max_y);
// Focus on middle 40% of body height for waist detection
uint8_t body_height = max_y - min_y + 1;
uint8_t waist_start = min_y + (body_height * 3) / 10; // 30% from top
uint8_t waist_end = min_y + (body_height * 7) / 10; // 70% from top
// Find row with maximum pressure in waist region
uint32_t max_row_sum = 0;
uint8_t waist_row = (waist_start + waist_end) / 2;
for (uint8_t y = waist_start; y <= waist_end && y <= max_y; y++)
{
uint32_t row_sum = 0;
for (uint8_t x = min_x; x <= max_x; x++)
{
int idx = y * MAT_WIDTH + x;
if (labeled_regions[idx] == body_label)
{
row_sum += sensor_data[idx];
}
}
if (row_sum > max_row_sum)
{
max_row_sum = row_sum;
waist_row = y;
}
}
// Calculate weighted center in waist row
uint32_t sum_x = 0, sum_weight = 0;
for (uint8_t x = min_x; x <= max_x; x++)
{
int idx = waist_row * MAT_WIDTH + x;
if (labeled_regions[idx] == body_label)
{
uint16_t weight = sensor_data[idx];
sum_x += x * weight;
sum_weight += weight;
}
}
Point_t waist_center = {0, 0};
if (sum_weight > 0)
{
waist_center.x_mm = (sum_x * POINT_SPACING_X_MM) / sum_weight;
waist_center.y_mm = waist_row * POINT_SPACING_Y_MM;
}
return waist_center;
}
/*
* Classify posture using simple heuristics
*/
static uint8_t classify_posture(uint8_t body_label, uint8_t *confidence)
{
uint8_t min_x, min_y, max_x, max_y;
get_bounding_box(body_label, &min_x, &min_y, &max_x, &max_y);
uint16_t width = (max_x - min_x + 1) * POINT_SPACING_X_MM;
uint16_t height = (max_y - min_y + 1) * POINT_SPACING_Y_MM;
// Calculate aspect ratio (width/height) * 100 for integer math
uint16_t aspect_ratio_x100 = (width * 100) / height;
// Heuristic classification
if (aspect_ratio_x100 > 150)
{ // aspect_ratio > 1.5
*confidence = 70;
return 0; // supine
}
else if (aspect_ratio_x100 < 120)
{ // aspect_ratio < 1.2
*confidence = 70;
return 1; // side
}
else
{
*confidence = 50;
return (aspect_ratio_x100 > 130) ? 0 : 1; // uncertain
}
}
/*
* Main analysis function
*
* @param sensor_data: Array of 260 pressure sensor readings (0-1023)
* @param result: Pointer to store analysis results
* @return: true if analysis successful, false otherwise
*/
uint16_t smoothed_data[TOTAL_SENSORS] = {0};
bool analyze_body_posture(const uint16_t *sensor_data, AnalysisResult_t *result)
{
if (!sensor_data || !result)
{
return false;
}
// Initialize result
rt_memset(result, 0, sizeof(AnalysisResult_t));
rt_memset(smoothed_data, 0, sizeof(smoothed_data));
// Apply smoothing filter
smooth_sensor_data(sensor_data, smoothed_data);
// Create binary mask
create_binary_mask(smoothed_data);
// Detect body regions
uint8_t num_bodies = detect_body_regions(smoothed_data);
result->num_bodies = (num_bodies > MAX_BODIES) ? MAX_BODIES : num_bodies;
// Analyze each detected body
uint8_t body_idx = 0;
for (uint8_t label = 2; label < (2 + num_bodies) && body_idx < MAX_BODIES; label++)
{
BodyInfo_t *body = &result->bodies[body_idx];
body->body_id = body_idx + 1;
// Calculate center of mass
body->center_of_mass = calculate_center_of_mass(smoothed_data, label);
// Estimate waist center
body->waist_center = estimate_waist_center(smoothed_data, label);
// Get dimensions
uint8_t min_x, min_y, max_x, max_y;
get_bounding_box(label, &min_x, &min_y, &max_x, &max_y);
body->width_mm = (max_x - min_x + 1) * POINT_SPACING_X_MM;
body->height_mm = (max_y - min_y + 1) * POINT_SPACING_Y_MM;
// Calculate total pressure
body->total_pressure = 0;
for (int i = 0; i < TOTAL_SENSORS; i++)
{
if (labeled_regions[i] == label)
{
body->total_pressure += smoothed_data[i];
}
}
// Classify posture
body->posture = classify_posture(label, &body->confidence);
body_idx++;
}
/* */
return true;
}
/*
* Get posture string for debugging
*/
const char *get_posture_string(uint8_t posture)
{
return (posture == 0) ? "supine" : "side";
}
/*
* Print analysis results (for debugging)
* Note: Requires printf implementation on MCU
*/
#ifdef DEBUG_PRINT
#include <stdio.h>
void print_analysis_results(const AnalysisResult_t *result)
{
rt_kprintf("=== Body Analysis Results ===\n");
rt_kprintf("Bodies detected: %d\n", result->num_bodies);
for (int i = 0; i < result->num_bodies; i++)
{
const BodyInfo_t *body = &result->bodies[i];
rt_kprintf("\nBody %d:\n", body->body_id);
rt_kprintf(" Posture: %s (confidence: %d%%)\n",
get_posture_string(body->posture), body->confidence);
rt_kprintf(" Waist center: (%d, %d) mm\n",
body->waist_center.x_mm, body->waist_center.y_mm);
rt_kprintf(" Center of mass: (%d, %d) mm\n",
body->center_of_mass.x_mm, body->center_of_mass.y_mm);
rt_kprintf(" Dimensions: %d x %d mm\n",
body->width_mm, body->height_mm);
rt_kprintf(" Total pressure: %lu\n", body->total_pressure);
}
}
#endif

84
CAN/XM-01/Core/Src/myEdge_ai_app.c
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@ -6,6 +6,7 @@
#include "mySensor_deal.h" #include "mySensor_deal.h"
#include "myEdge_ai_app.h" #include "myEdge_ai_app.h"
#include "app_x-cube-ai.h" #include "app_x-cube-ai.h"
#include "mcu_body_analyzer.h"
model_t model; model_t model;
ai_handle network; ai_handle network;
@ -101,6 +102,68 @@ void preprocess_data(const float *input, float *output, size_t length)
output[i] = (tanh_val + 1.0f) / 2.0f; output[i] = (tanh_val + 1.0f) / 2.0f;
} }
} }
/*
* Process analysis results (implement your application logic here)
*/
void process_analysis_results(const AnalysisResult_t *result)
{
static uint8_t previous_body_count = 0;
// Check for body count changes
if (result->num_bodies != previous_body_count)
{
rt_kprintf("Body count changed: %d -> %d\n",
previous_body_count, result->num_bodies);
previous_body_count = result->num_bodies;
// You could trigger events here:
// - Send notification to central system
// - Update display
// - Change LED indicators
// - Log to memory/SD card
}
// Process each detected body
for (int i = 0; i < result->num_bodies; i++)
{
const BodyInfo_t *body = &result->bodies[i];
// Example: Check for posture changes
static uint8_t previous_posture[MAX_BODIES] = {0xFF, 0xFF};
if (previous_posture[i] != body->posture)
{
rt_kprintf("Body %d posture changed to: %s\n",
body->body_id, get_posture_string(body->posture));
previous_posture[i] = body->posture;
// You could implement alerts here:
// - Posture change notification
// - Sleep quality tracking
// - Health monitoring alerts
}
// Example: Monitor waist position for movement detection
static Point_t previous_waist[MAX_BODIES];
uint16_t movement_x = abs((int16_t)body->waist_center.x_mm -
(int16_t)previous_waist[i].x_mm);
uint16_t movement_y = abs((int16_t)body->waist_center.y_mm -
(int16_t)previous_waist[i].y_mm);
if (movement_x > 50 || movement_y > 50)
{ // Movement > 5cm
rt_kprintf("Body %d significant movement detected\n", body->body_id);
// You could implement movement tracking here:
// - Sleep restlessness monitoring
// - Position adjustment alerts
// - Activity logging
}
previous_waist[i] = body->waist_center;
}
}
//////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////
int aiInit(void) int aiInit(void)
{ {
@ -149,17 +212,34 @@ int AiModel(uint8_t *input)
rt_tick_t End_Tick = 0; rt_tick_t End_Tick = 0;
int predicted_class = 0; int predicted_class = 0;
float max_confidence = 0.0f; float max_confidence = 0.0f;
AnalysisResult_t analysis_result;
rt_kprintf("\nStart Test Code\n"); rt_kprintf("\nStart Test Code\n");
// 转Float
PictureCharArrayToFloat(input, inputBuf, AI_MODEL_IN_1_SIZE); PictureCharArrayToFloat(input, inputBuf, AI_MODEL_IN_1_SIZE);
// 数据前置处理
preprocess_data((const float *)inputBuf, aiInData, AI_MODEL_IN_1_SIZE); preprocess_data((const float *)inputBuf, aiInData, AI_MODEL_IN_1_SIZE);
// 运算AI模型
Start_Tick = rt_tick_get(); Start_Tick = rt_tick_get();
aiRun(aiInData, aiOutData); aiRun(aiInData, aiOutData);
End_Tick = rt_tick_get(); End_Tick = rt_tick_get();
rt_kprintf("AI model UseTime : %ld ms\n", End_Tick - Start_Tick); rt_kprintf("AI model UseTime : %ld ms\n", End_Tick - Start_Tick);
// 计算腰部位置
if (analyze_body_posture((const uint16_t *)input, &analysis_result))
{
uint32_t analysis_duration = rt_tick_get();
rt_kprintf("Analysis completed in %lu ms\n", analysis_duration);
// Process the results
process_analysis_results(&analysis_result);
#ifdef DEBUG_PRINT
print_analysis_results(&analysis_result);
#endif
}
else
rt_kprintf("Analysis failed\n");
for (int i = 0; i < AI_MODEL_OUT_1_SIZE; i++) for (int i = 0; i < AI_MODEL_OUT_1_SIZE; i++)
{ {
if (aiOutData[i] > max_confidence) if (aiOutData[i] > max_confidence)

2
CAN/XM-01/Core/Src/myMattress_ctrl.c
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@ -185,4 +185,4 @@ int remote_thread_init(void)
rt_thread_startup(&remote_thread); // 启动线程 rt_thread_startup(&remote_thread); // 启动线程
return 0; return 0;
} }
INIT_APP_EXPORT(remote_thread_init); // 启动自动初始化 // INIT_APP_EXPORT(remote_thread_init); // 启动自动初始化

40
CAN/XM-01/MDK-ARM/XM-01.uvguix.admin
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@ -100,8 +100,8 @@
<flags>2</flags> <flags>2</flags>
<showCmd>3</showCmd> <showCmd>3</showCmd>
<MinPosition> <MinPosition>
<xPos>-1</xPos> <xPos>-32000</xPos>
<yPos>-1</yPos> <yPos>-32000</yPos>
</MinPosition> </MinPosition>
<MaxPosition> <MaxPosition>
<xPos>-1</xPos> <xPos>-1</xPos>
@ -117,8 +117,8 @@
<MDIClientArea> <MDIClientArea>
<RegID>0</RegID> <RegID>0</RegID>
<MDITabState> <MDITabState>
<Len>2050</Len> <Len>2256</Len>
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5
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8039
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File diff suppressed because it is too large Load Diff

1
README.md
View File

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| 2025-05-26 | CAN版本260点增加模型完成 | | | 2025-05-26 | CAN版本260点增加模型完成 | |
| 2025-05-27 | 串口部分处理增加完成，未验证 | | | 2025-05-27 | 串口部分处理增加完成，未验证 | |
| | CAN总线调试完成 可以通讯 完成线程 | | | | CAN总线调试完成 可以通讯 完成线程 | |
| 2025-06-03 | 完成算法计算腰部位置代码移植 | 后续还需要更改 |
--- ---