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| import numpy as np
class CapacitiveHoD:
"""
电容式离手检测信号处理器
"""
def __init__(self, num_channels: int = 4):
self.num_channels = num_channels
self.baseline = np.zeros(num_channels)
self.touch_threshold = 50
self.grip_threshold = 100
self.debounce_count = np.zeros(num_channels, dtype=int)
self.debounce_limit = 3
def calibrate(self, baseline_readings: np.ndarray):
"""基线校准（无手时）"""
self.baseline = baseline_readings
def classify(self, readings: np.ndarray) -> dict:
"""
分类握持状态
Args:
readings: [num_channels] 原始ADC读数
Returns:
{
'hands_on': bool,
'grip_type': str,
'grip_strength': float,
'channels_active': list
}
"""
delta = readings - self.baseline
channels_active = []
for i in range(self.num_channels):
if delta[i] > self.grip_threshold:
channels_active.append(i)
self.debounce_count[i] = min(self.debounce_count[i] + 1,
self.debounce_limit)
elif delta[i] > self.touch_threshold:
channels_active.append(i)
self.debounce_count[i] = min(self.debounce_count[i] + 1,
self.debounce_limit)
else:
self.debounce_count[i] = max(self.debounce_count[i] - 1, 0)
confirmed_active = [i for i in channels_active
if self.debounce_count[i] >= self.debounce_limit]
hands_on = len(confirmed_active) > 0
grip_type = self._classify_grip(confirmed_active)
if len(confirmed_active) > 0:
grip_strength = float(np.mean(delta[confirmed_active])) / 200.0
else:
grip_strength = 0.0
return {
'hands_on': hands_on,
'grip_type': grip_type,
'grip_strength': min(grip_strength, 1.0),
'channels_active': confirmed_active,
'raw_delta': delta.tolist()
}
def _classify_grip(self, active_channels: list) -> str:
"""识别握持类型"""
if len(active_channels) == 0:
return 'none'
elif len(active_channels) == 1:
return 'single_hand'
elif len(active_channels) == 2:
if 0 in active_channels and 1 in active_channels:
return 'left_hand'
elif 2 in active_channels and 3 in active_channels:
return 'right_hand'
elif 0 in active_channels and 3 in active_channels:
return 'cross_grip'
else:
return 'two_hand'
else:
return 'full_grip'
if __name__ == "__main__":
hod = CapacitiveHoD(num_channels=4)
hod.calibrate(np.array([100, 100, 100, 100], dtype=float))
result = hod.classify(np.array([102, 101, 98, 103]))
print(f"无手: hands_on={result['hands_on']}, type={result['grip_type']}")
result = hod.classify(np.array([180, 170, 105, 103]))
print(f"左手: hands_on={result['hands_on']}, type={result['grip_type']}")
result = hod.classify(np.array([175, 165, 180, 170]))
print(f"双手: hands_on={result['hands_on']}, type={result['grip_type']}")
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